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convGAN-majority-5
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folding_yeast5.log

folding_yeast5.log 162 KB
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
  1. 

  2. 

  3. ///////////////////////////////////////////
    4. 

  4. // Running convGAN-majority-5 on folding_yeast5
    5. 

  5. ///////////////////////////////////////////
    6. 

  6. 

  7. Load 'data_input/folding_yeast5'
    8. 

  8. from pickle file
    9. 

  9. Data loaded.
    10. 

  10. -> Shuffling data
    11. 

  11. ### Start exercise for synthetic point generator
    12. 

  12. 

  13. ====== Step 1/5 =======
    14. 

  14. -> Shuffling data
    15. 

  15. -> Spliting data to slices
    16. 

  16. 

  17. ------ Step 1/5: Slice 1/5 -------
    18. 

  18. -> Reset the GAN
    19. 

  19. -> Train generator for synthetic samples
    20. 

  20. -> create 1117 synthetic samples
    21. 

  21. -> retrain GAN for predict
    22. 

  22. Epoch 1/10
    23. 

  23. 
  1/116 [..............................] - ETA: 19s - loss: 0.0510
 33/116 [=======>......................] - ETA: 0s - loss: 0.0978 
 68/116 [================>.............] - ETA: 0s - loss: 0.0895
100/116 [========================>.....] - ETA: 0s - loss: 0.0909
116/116 [==============================] - 0s 2ms/step - loss: 0.0920
    24. 

  24. Epoch 2/10
    25. 

  25. 
  1/116 [..............................] - ETA: 0s - loss: 0.0632
 33/116 [=======>......................] - ETA: 0s - loss: 0.0889
 62/116 [===============>..............] - ETA: 0s - loss: 0.0995
 96/116 [=======================>......] - ETA: 0s - loss: 0.0921
116/116 [==============================] - 0s 2ms/step - loss: 0.0913
    26. 

  26. Epoch 3/10
    27. 

  27. 
  1/116 [..............................] - ETA: 0s - loss: 0.0353
 36/116 [========>.....................] - ETA: 0s - loss: 0.0976
 67/116 [================>.............] - ETA: 0s - loss: 0.0953
 97/116 [========================>.....] - ETA: 0s - loss: 0.0899
116/116 [==============================] - 0s 2ms/step - loss: 0.0907
    28. 

  28. Epoch 4/10
    29. 

  29. 
  1/116 [..............................] - ETA: 0s - loss: 0.0399
 30/116 [======>.......................] - ETA: 0s - loss: 0.0786
 63/116 [===============>..............] - ETA: 0s - loss: 0.0938
 97/116 [========================>.....] - ETA: 0s - loss: 0.0893
116/116 [==============================] - 0s 2ms/step - loss: 0.0889
    30. 

  30. Epoch 5/10
    31. 

  31. 
  1/116 [..............................] - ETA: 0s - loss: 0.0366
 33/116 [=======>......................] - ETA: 0s - loss: 0.0773
 68/116 [================>.............] - ETA: 0s - loss: 0.0815
101/116 [=========================>....] - ETA: 0s - loss: 0.0851
116/116 [==============================] - 0s 2ms/step - loss: 0.0867
    32. 

  32. Epoch 6/10
    33. 

  33. 
  1/116 [..............................] - ETA: 0s - loss: 0.0246
 31/116 [=======>......................] - ETA: 0s - loss: 0.0834
 65/116 [===============>..............] - ETA: 0s - loss: 0.0895
 98/116 [========================>.....] - ETA: 0s - loss: 0.0853
116/116 [==============================] - 0s 2ms/step - loss: 0.0859
    34. 

  34. Epoch 7/10
    35. 

  35. 
  1/116 [..............................] - ETA: 0s - loss: 0.0370
 29/116 [======>.......................] - ETA: 0s - loss: 0.0710
 57/116 [=============>................] - ETA: 0s - loss: 0.0889
 85/116 [====================>.........] - ETA: 0s - loss: 0.0863
116/116 [==============================] - 0s 2ms/step - loss: 0.0867
    36. 

  36. Epoch 8/10
    37. 

  37. 
  1/116 [..............................] - ETA: 0s - loss: 0.0312
 31/116 [=======>......................] - ETA: 0s - loss: 0.0834
 63/116 [===============>..............] - ETA: 0s - loss: 0.0799
 97/116 [========================>.....] - ETA: 0s - loss: 0.0914
116/116 [==============================] - 0s 2ms/step - loss: 0.0844
    38. 

  38. Epoch 9/10
    39. 

  39. 
  1/116 [..............................] - ETA: 0s - loss: 0.1040
 33/116 [=======>......................] - ETA: 0s - loss: 0.0801
 66/116 [================>.............] - ETA: 0s - loss: 0.0951
 99/116 [========================>.....] - ETA: 0s - loss: 0.0870
116/116 [==============================] - 0s 2ms/step - loss: 0.0836
    40. 

  40. Epoch 10/10
    41. 

  41. 
  1/116 [..............................] - ETA: 0s - loss: 0.0166
 34/116 [=======>......................] - ETA: 0s - loss: 0.0790
 64/116 [===============>..............] - ETA: 0s - loss: 0.0768
 94/116 [=======================>......] - ETA: 0s - loss: 0.0756
116/116 [==============================] - 0s 2ms/step - loss: 0.0824
    42. 

  42. -> test with GAN.predict
    43. 

  43. GAN tn, fp: 280, 8
    44. 

  44. GAN fn, tp: 1, 8
    45. 

  45. GAN f1 score: 0.640
    46. 

  46. GAN cohens kappa score: 0.625
    47. 

  47. 

  48. -> test with 'LR'
    49. 

  49. LR tn, fp: 275, 13
    50. 

  50. LR fn, tp: 1, 8
    51. 

  51. LR f1 score: 0.533
    52. 

  52. LR cohens kappa score: 0.513
    53. 

  53. LR average precision score: 0.876
    54. 

  54. 

  55. -> test with 'RF'
    56. 

  56. RF tn, fp: 288, 0
    57. 

  57. RF fn, tp: 6, 3
    58. 

  58. RF f1 score: 0.500
    59. 

  59. RF cohens kappa score: 0.492
    60. 

  60. 

  61. -> test with 'GB'
    62. 

  62. GB tn, fp: 287, 1
    63. 

  63. GB fn, tp: 3, 6
    64. 

  64. GB f1 score: 0.750
    65. 

  65. GB cohens kappa score: 0.743
    66. 

  66. 

  67. -> test with 'KNN'
    68. 

  68. KNN tn, fp: 282, 6
    69. 

  69. KNN fn, tp: 0, 9
    70. 

  70. KNN f1 score: 0.750
    71. 

  71. KNN cohens kappa score: 0.740
    72. 

  72. 

  73. 

  74. ------ Step 1/5: Slice 2/5 -------
    75. 

  75. -> Reset the GAN
    76. 

  76. -> Train generator for synthetic samples
    77. 

  77. -> create 1117 synthetic samples
    78. 

  78. -> retrain GAN for predict
    79. 

  79. Epoch 1/10
    80. 

  80. 
  1/116 [..............................] - ETA: 21s - loss: 0.0801
 37/116 [========>.....................] - ETA: 0s - loss: 0.0666 
 73/116 [=================>............] - ETA: 0s - loss: 0.0657
110/116 [===========================>..] - ETA: 0s - loss: 0.0750
116/116 [==============================] - 0s 1ms/step - loss: 0.0744
    81. 

  81. Epoch 2/10
    82. 

  82. 
  1/116 [..............................] - ETA: 0s - loss: 0.2249
 36/116 [========>.....................] - ETA: 0s - loss: 0.0499
 72/116 [=================>............] - ETA: 0s - loss: 0.0693
108/116 [==========================>...] - ETA: 0s - loss: 0.0728
116/116 [==============================] - 0s 1ms/step - loss: 0.0730
    83. 

  83. Epoch 3/10
    84. 

  84. 
  1/116 [..............................] - ETA: 0s - loss: 0.1034
 38/116 [========>.....................] - ETA: 0s - loss: 0.0702
 75/116 [==================>...........] - ETA: 0s - loss: 0.0733
111/116 [===========================>..] - ETA: 0s - loss: 0.0722
116/116 [==============================] - 0s 1ms/step - loss: 0.0722
    85. 

  85. Epoch 4/10
    86. 

  86. 
  1/116 [..............................] - ETA: 0s - loss: 0.0266
 37/116 [========>.....................] - ETA: 0s - loss: 0.0879
 74/116 [==================>...........] - ETA: 0s - loss: 0.0766
115/116 [============================>.] - ETA: 0s - loss: 0.0719
116/116 [==============================] - 0s 1ms/step - loss: 0.0718
    87. 

  87. Epoch 5/10
    88. 

  88. 
  1/116 [..............................] - ETA: 0s - loss: 0.3760
 11/116 [=>............................] - ETA: 0s - loss: 0.0791
 40/116 [=========>....................] - ETA: 0s - loss: 0.0825
 81/116 [===================>..........] - ETA: 0s - loss: 0.0724
116/116 [==============================] - 0s 2ms/step - loss: 0.0693
    89. 

  89. Epoch 6/10
    90. 

  90. 
  1/116 [..............................] - ETA: 0s - loss: 0.0107
 37/116 [========>.....................] - ETA: 0s - loss: 0.0682
 79/116 [===================>..........] - ETA: 0s - loss: 0.0665
116/116 [==============================] - 0s 1ms/step - loss: 0.0700
    91. 

  91. Epoch 7/10
    92. 

  92. 
  1/116 [..............................] - ETA: 0s - loss: 0.0578
 42/116 [=========>....................] - ETA: 0s - loss: 0.0483
 85/116 [====================>.........] - ETA: 0s - loss: 0.0654
116/116 [==============================] - 0s 1ms/step - loss: 0.0682
    93. 

  93. Epoch 8/10
    94. 

  94. 
  1/116 [..............................] - ETA: 0s - loss: 0.0145
 41/116 [=========>....................] - ETA: 0s - loss: 0.0585
 83/116 [====================>.........] - ETA: 0s - loss: 0.0602
116/116 [==============================] - 0s 1ms/step - loss: 0.0683
    95. 

  95. Epoch 9/10
    96. 

  96. 
  1/116 [..............................] - ETA: 0s - loss: 0.1089
 41/116 [=========>....................] - ETA: 0s - loss: 0.0566
 80/116 [===================>..........] - ETA: 0s - loss: 0.0612
116/116 [==============================] - 0s 1ms/step - loss: 0.0662
    97. 

  97. Epoch 10/10
    98. 

  98. 
  1/116 [..............................] - ETA: 0s - loss: 0.0720
 43/116 [==========>...................] - ETA: 0s - loss: 0.0580
 84/116 [====================>.........] - ETA: 0s - loss: 0.0678
116/116 [==============================] - 0s 1ms/step - loss: 0.0659
    99. 

  99. -> test with GAN.predict
    100. 

  100. GAN tn, fp: 271, 17
    101. 

  101. GAN fn, tp: 0, 9
    102. 

  102. GAN f1 score: 0.514
    103. 

  103. GAN cohens kappa score: 0.491
    104. 

  104. 

  105. -> test with 'LR'
    106. 

  106. LR tn, fp: 273, 15
    107. 

  107. LR fn, tp: 0, 9
    108. 

  108. LR f1 score: 0.545
    109. 

  109. LR cohens kappa score: 0.524
    110. 

  110. LR average precision score: 0.701
    111. 

  111. 

  112. -> test with 'RF'
    113. 

  113. RF tn, fp: 285, 3
    114. 

  114. RF fn, tp: 2, 7
    115. 

  115. RF f1 score: 0.737
    116. 

  116. RF cohens kappa score: 0.728
    117. 

  117. 

  118. -> test with 'GB'
    119. 

  119. GB tn, fp: 285, 3
    120. 

  120. GB fn, tp: 1, 8
    121. 

  121. GB f1 score: 0.800
    122. 

  122. GB cohens kappa score: 0.793
    123. 

  123. 

  124. -> test with 'KNN'
    125. 

  125. KNN tn, fp: 274, 14
    126. 

  126. KNN fn, tp: 0, 9
    127. 

  127. KNN f1 score: 0.562
    128. 

  128. KNN cohens kappa score: 0.543
    129. 

  129. 

  130. 

  131. ------ Step 1/5: Slice 3/5 -------
    132. 

  132. -> Reset the GAN
    133. 

  133. -> Train generator for synthetic samples
    134. 

  134. -> create 1117 synthetic samples
    135. 

  135. -> retrain GAN for predict
    136. 

  136. Epoch 1/10
    137. 

  137. 
  1/116 [..............................] - ETA: 22s - loss: 0.0072
 32/116 [=======>......................] - ETA: 0s - loss: 0.0748 
 65/116 [===============>..............] - ETA: 0s - loss: 0.0868
 97/116 [========================>.....] - ETA: 0s - loss: 0.0751
116/116 [==============================] - 0s 2ms/step - loss: 0.0735
    138. 

  138. Epoch 2/10
    139. 

  139. 
  1/116 [..............................] - ETA: 0s - loss: 0.0216
 33/116 [=======>......................] - ETA: 0s - loss: 0.0833
 64/116 [===============>..............] - ETA: 0s - loss: 0.0695
 97/116 [========================>.....] - ETA: 0s - loss: 0.0729
116/116 [==============================] - 0s 2ms/step - loss: 0.0715
    140. 

  140. Epoch 3/10
    141. 

  141. 
  1/116 [..............................] - ETA: 0s - loss: 0.0391
 34/116 [=======>......................] - ETA: 0s - loss: 0.0717
 67/116 [================>.............] - ETA: 0s - loss: 0.0758
101/116 [=========================>....] - ETA: 0s - loss: 0.0731
116/116 [==============================] - 0s 2ms/step - loss: 0.0701
    142. 

  142. Epoch 4/10
    143. 

  143. 
  1/116 [..............................] - ETA: 0s - loss: 0.0079
 34/116 [=======>......................] - ETA: 0s - loss: 0.0631
 66/116 [================>.............] - ETA: 0s - loss: 0.0661
 98/116 [========================>.....] - ETA: 0s - loss: 0.0693
116/116 [==============================] - 0s 2ms/step - loss: 0.0701
    144. 

  144. Epoch 5/10
    145. 

  145. 
  1/116 [..............................] - ETA: 0s - loss: 0.0387
 32/116 [=======>......................] - ETA: 0s - loss: 0.0673
 63/116 [===============>..............] - ETA: 0s - loss: 0.0744
 94/116 [=======================>......] - ETA: 0s - loss: 0.0702
116/116 [==============================] - 0s 2ms/step - loss: 0.0692
    146. 

  146. Epoch 6/10
    147. 

  147. 
  1/116 [..............................] - ETA: 0s - loss: 0.0874
 33/116 [=======>......................] - ETA: 0s - loss: 0.0610
 63/116 [===============>..............] - ETA: 0s - loss: 0.0546
 95/116 [=======================>......] - ETA: 0s - loss: 0.0692
116/116 [==============================] - 0s 2ms/step - loss: 0.0687
    148. 

  148. Epoch 7/10
    149. 

  149. 
  1/116 [..............................] - ETA: 0s - loss: 0.0299
 32/116 [=======>......................] - ETA: 0s - loss: 0.0678
 65/116 [===============>..............] - ETA: 0s - loss: 0.0713
100/116 [========================>.....] - ETA: 0s - loss: 0.0712
116/116 [==============================] - 0s 1ms/step - loss: 0.0696
    150. 

  150. Epoch 8/10
    151. 

  151. 
  1/116 [..............................] - ETA: 0s - loss: 0.2908
 32/116 [=======>......................] - ETA: 0s - loss: 0.0850
 64/116 [===============>..............] - ETA: 0s - loss: 0.0694
 96/116 [=======================>......] - ETA: 0s - loss: 0.0672
116/116 [==============================] - 0s 2ms/step - loss: 0.0660
    152. 

  152. Epoch 9/10
    153. 

  153. 
  1/116 [..............................] - ETA: 0s - loss: 0.0179
 37/116 [========>.....................] - ETA: 0s - loss: 0.0680
 69/116 [================>.............] - ETA: 0s - loss: 0.0766
100/116 [========================>.....] - ETA: 0s - loss: 0.0707
116/116 [==============================] - 0s 2ms/step - loss: 0.0651
    154. 

  154. Epoch 10/10
    155. 

  155. 
  1/116 [..............................] - ETA: 0s - loss: 0.1692
 36/116 [========>.....................] - ETA: 0s - loss: 0.0644
 68/116 [================>.............] - ETA: 0s - loss: 0.0744
102/116 [=========================>....] - ETA: 0s - loss: 0.0684
116/116 [==============================] - 0s 2ms/step - loss: 0.0676
    156. 

  156. -> test with GAN.predict
    157. 

  157. GAN tn, fp: 277, 11
    158. 

  158. GAN fn, tp: 1, 8
    159. 

  159. GAN f1 score: 0.571
    160. 

  160. GAN cohens kappa score: 0.553
    161. 

  161. 

  162. -> test with 'LR'
    163. 

  163. LR tn, fp: 278, 10
    164. 

  164. LR fn, tp: 0, 9
    165. 

  165. LR f1 score: 0.643
    166. 

  166. LR cohens kappa score: 0.628
    167. 

  167. LR average precision score: 0.587
    168. 

  168. 

  169. -> test with 'RF'
    170. 

  170. RF tn, fp: 286, 2
    171. 

  171. RF fn, tp: 4, 5
    172. 

  172. RF f1 score: 0.625
    173. 

  173. RF cohens kappa score: 0.615
    174. 

  174. 

  175. -> test with 'GB'
    176. 

  176. GB tn, fp: 285, 3
    177. 

  177. GB fn, tp: 3, 6
    178. 

  178. GB f1 score: 0.667
    179. 

  179. GB cohens kappa score: 0.656
    180. 

  180. 

  181. -> test with 'KNN'
    182. 

  182. KNN tn, fp: 277, 11
    183. 

  183. KNN fn, tp: 1, 8
    184. 

  184. KNN f1 score: 0.571
    185. 

  185. KNN cohens kappa score: 0.553
    186. 

  186. 

  187. 

  188. ------ Step 1/5: Slice 4/5 -------
    189. 

  189. -> Reset the GAN
    190. 

  190. -> Train generator for synthetic samples
    191. 

  191. -> create 1117 synthetic samples
    192. 

  192. -> retrain GAN for predict
    193. 

  193. Epoch 1/10
    194. 

  194. 
  1/116 [..............................] - ETA: 19s - loss: 0.0876
 39/116 [=========>....................] - ETA: 0s - loss: 0.0756 
 75/116 [==================>...........] - ETA: 0s - loss: 0.0741
111/116 [===========================>..] - ETA: 0s - loss: 0.0780
116/116 [==============================] - 0s 1ms/step - loss: 0.0785
    195. 

  195. Epoch 2/10
    196. 

  196. 
  1/116 [..............................] - ETA: 0s - loss: 0.0191
 37/116 [========>.....................] - ETA: 0s - loss: 0.0569
 69/116 [================>.............] - ETA: 0s - loss: 0.0597
101/116 [=========================>....] - ETA: 0s - loss: 0.0721
116/116 [==============================] - 0s 2ms/step - loss: 0.0754
    197. 

  197. Epoch 3/10
    198. 

  198. 
  1/116 [..............................] - ETA: 0s - loss: 0.0424
 36/116 [========>.....................] - ETA: 0s - loss: 0.0610
 67/116 [================>.............] - ETA: 0s - loss: 0.0762
 99/116 [========================>.....] - ETA: 0s - loss: 0.0703
116/116 [==============================] - 0s 2ms/step - loss: 0.0755
    199. 

  199. Epoch 4/10
    200. 

  200. 
  1/116 [..............................] - ETA: 0s - loss: 0.0217
 36/116 [========>.....................] - ETA: 0s - loss: 0.0530
 73/116 [=================>............] - ETA: 0s - loss: 0.0580
110/116 [===========================>..] - ETA: 0s - loss: 0.0717
116/116 [==============================] - 0s 1ms/step - loss: 0.0742
    201. 

  201. Epoch 5/10
    202. 

  202. 
  1/116 [..............................] - ETA: 0s - loss: 0.0300
 35/116 [========>.....................] - ETA: 0s - loss: 0.0601
 73/116 [=================>............] - ETA: 0s - loss: 0.0766
111/116 [===========================>..] - ETA: 0s - loss: 0.0744
116/116 [==============================] - 0s 1ms/step - loss: 0.0732
    203. 

  203. Epoch 6/10
    204. 

  204. 
  1/116 [..............................] - ETA: 0s - loss: 0.0071
 39/116 [=========>....................] - ETA: 0s - loss: 0.0844
 76/116 [==================>...........] - ETA: 0s - loss: 0.0812
109/116 [===========================>..] - ETA: 0s - loss: 0.0739
116/116 [==============================] - 0s 1ms/step - loss: 0.0720
    205. 

  205. Epoch 7/10
    206. 

  206. 
  1/116 [..............................] - ETA: 0s - loss: 0.0398
 35/116 [========>.....................] - ETA: 0s - loss: 0.0607
 69/116 [================>.............] - ETA: 0s - loss: 0.0646
101/116 [=========================>....] - ETA: 0s - loss: 0.0724
116/116 [==============================] - 0s 2ms/step - loss: 0.0729
    207. 

  207. Epoch 8/10
    208. 

  208. 
  1/116 [..............................] - ETA: 0s - loss: 0.2263
 38/116 [========>.....................] - ETA: 0s - loss: 0.0645
 70/116 [=================>............] - ETA: 0s - loss: 0.0723
101/116 [=========================>....] - ETA: 0s - loss: 0.0747
116/116 [==============================] - 0s 2ms/step - loss: 0.0724
    209. 

  209. Epoch 9/10
    210. 

  210. 
  1/116 [..............................] - ETA: 0s - loss: 0.0389
 35/116 [========>.....................] - ETA: 0s - loss: 0.0692
 72/116 [=================>............] - ETA: 0s - loss: 0.0673
108/116 [==========================>...] - ETA: 0s - loss: 0.0713
116/116 [==============================] - 0s 1ms/step - loss: 0.0704
    211. 

  211. Epoch 10/10
    212. 

  212. 
  1/116 [..............................] - ETA: 0s - loss: 0.2030
 36/116 [========>.....................] - ETA: 0s - loss: 0.0595
 72/116 [=================>............] - ETA: 0s - loss: 0.0710
107/116 [==========================>...] - ETA: 0s - loss: 0.0699
116/116 [==============================] - 0s 1ms/step - loss: 0.0694
    213. 

  213. -> test with GAN.predict
    214. 

  214. GAN tn, fp: 282, 6
    215. 

  215. GAN fn, tp: 3, 6
    216. 

  216. GAN f1 score: 0.571
    217. 

  217. GAN cohens kappa score: 0.556
    218. 

  218. 

  219. -> test with 'LR'
    220. 

  220. LR tn, fp: 281, 7
    221. 

  221. LR fn, tp: 0, 9
    222. 

  222. LR f1 score: 0.720
    223. 

  223. LR cohens kappa score: 0.709
    224. 

  224. LR average precision score: 0.771
    225. 

  225. 

  226. -> test with 'RF'
    227. 

  227. RF tn, fp: 288, 0
    228. 

  228. RF fn, tp: 3, 6
    229. 

  229. RF f1 score: 0.800
    230. 

  230. RF cohens kappa score: 0.795
    231. 

  231. 

  232. -> test with 'GB'
    233. 

  233. GB tn, fp: 288, 0
    234. 

  234. GB fn, tp: 4, 5
    235. 

  235. GB f1 score: 0.714
    236. 

  236. GB cohens kappa score: 0.708
    237. 

  237. 

  238. -> test with 'KNN'
    239. 

  239. KNN tn, fp: 285, 3
    240. 

  240. KNN fn, tp: 0, 9
    241. 

  241. KNN f1 score: 0.857
    242. 

  242. KNN cohens kappa score: 0.852
    243. 

  243. 

  244. 

  245. ------ Step 1/5: Slice 5/5 -------
    246. 

  246. -> Reset the GAN
    247. 

  247. -> Train generator for synthetic samples
    248. 

  248. -> create 1116 synthetic samples
    249. 

  249. -> retrain GAN for predict
    250. 

  250. Epoch 1/10
    251. 

  251. 
  1/116 [..............................] - ETA: 19s - loss: 0.0736
 38/116 [========>.....................] - ETA: 0s - loss: 0.0821 
 74/116 [==================>...........] - ETA: 0s - loss: 0.0858
110/116 [===========================>..] - ETA: 0s - loss: 0.0823
116/116 [==============================] - 0s 1ms/step - loss: 0.0807
    252. 

  252. Epoch 2/10
    253. 

  253. 
  1/116 [..............................] - ETA: 0s - loss: 0.0460
 35/116 [========>.....................] - ETA: 0s - loss: 0.0727
 71/116 [=================>............] - ETA: 0s - loss: 0.0786
106/116 [==========================>...] - ETA: 0s - loss: 0.0831
116/116 [==============================] - 0s 1ms/step - loss: 0.0793
    254. 

  254. Epoch 3/10
    255. 

  255. 
  1/116 [..............................] - ETA: 0s - loss: 0.0640
 33/116 [=======>......................] - ETA: 0s - loss: 0.0987
 67/116 [================>.............] - ETA: 0s - loss: 0.0957
102/116 [=========================>....] - ETA: 0s - loss: 0.0807
116/116 [==============================] - 0s 1ms/step - loss: 0.0778
    256. 

  256. Epoch 4/10
    257. 

  257. 
  1/116 [..............................] - ETA: 0s - loss: 0.0157
 39/116 [=========>....................] - ETA: 0s - loss: 0.0762
 78/116 [===================>..........] - ETA: 0s - loss: 0.0853
113/116 [============================>.] - ETA: 0s - loss: 0.0792
116/116 [==============================] - 0s 1ms/step - loss: 0.0783
    258. 

  258. Epoch 5/10
    259. 

  259. 
  1/116 [..............................] - ETA: 0s - loss: 0.1219
 38/116 [========>.....................] - ETA: 0s - loss: 0.0805
 75/116 [==================>...........] - ETA: 0s - loss: 0.0724
112/116 [===========================>..] - ETA: 0s - loss: 0.0733
116/116 [==============================] - 0s 1ms/step - loss: 0.0764
    260. 

  260. Epoch 6/10
    261. 

  261. 
  1/116 [..............................] - ETA: 0s - loss: 0.2731
 37/116 [========>.....................] - ETA: 0s - loss: 0.0737
 75/116 [==================>...........] - ETA: 0s - loss: 0.0747
113/116 [============================>.] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0762
    262. 

  262. Epoch 7/10
    263. 

  263. 
  1/116 [..............................] - ETA: 0s - loss: 0.1061
 38/116 [========>.....................] - ETA: 0s - loss: 0.0782
 75/116 [==================>...........] - ETA: 0s - loss: 0.0715
109/116 [===========================>..] - ETA: 0s - loss: 0.0716
116/116 [==============================] - 0s 1ms/step - loss: 0.0747
    264. 

  264. Epoch 8/10
    265. 

  265. 
  1/116 [..............................] - ETA: 0s - loss: 0.0442
 39/116 [=========>....................] - ETA: 0s - loss: 0.0681
 76/116 [==================>...........] - ETA: 0s - loss: 0.0821
113/116 [============================>.] - ETA: 0s - loss: 0.0762
116/116 [==============================] - 0s 1ms/step - loss: 0.0763
    266. 

  266. Epoch 9/10
    267. 

  267. 
  1/116 [..............................] - ETA: 0s - loss: 0.1021
 39/116 [=========>....................] - ETA: 0s - loss: 0.0668
 77/116 [==================>...........] - ETA: 0s - loss: 0.0714
115/116 [============================>.] - ETA: 0s - loss: 0.0739
116/116 [==============================] - 0s 1ms/step - loss: 0.0738
    268. 

  268. Epoch 10/10
    269. 

  269. 
  1/116 [..............................] - ETA: 0s - loss: 0.0057
 39/116 [=========>....................] - ETA: 0s - loss: 0.0706
 73/116 [=================>............] - ETA: 0s - loss: 0.0647
109/116 [===========================>..] - ETA: 0s - loss: 0.0745
116/116 [==============================] - 0s 1ms/step - loss: 0.0727
    270. 

  270. -> test with GAN.predict
    271. 

  271. GAN tn, fp: 273, 15
    272. 

  272. GAN fn, tp: 0, 8
    273. 

  273. GAN f1 score: 0.516
    274. 

  274. GAN cohens kappa score: 0.496
    275. 

  275. 

  276. -> test with 'LR'
    277. 

  277. LR tn, fp: 273, 15
    278. 

  278. LR fn, tp: 0, 8
    279. 

  279. LR f1 score: 0.516
    280. 

  280. LR cohens kappa score: 0.496
    281. 

  281. LR average precision score: 0.689
    282. 

  282. 

  283. -> test with 'RF'
    284. 

  284. RF tn, fp: 286, 2
    285. 

  285. RF fn, tp: 3, 5
    286. 

  286. RF f1 score: 0.667
    287. 

  287. RF cohens kappa score: 0.658
    288. 

  288. 

  289. -> test with 'GB'
    290. 

  290. GB tn, fp: 286, 2
    291. 

  291. GB fn, tp: 2, 6
    292. 

  292. GB f1 score: 0.750
    293. 

  293. GB cohens kappa score: 0.743
    294. 

  294. 

  295. -> test with 'KNN'
    296. 

  296. KNN tn, fp: 276, 12
    297. 

  297. KNN fn, tp: 0, 8
    298. 

  298. KNN f1 score: 0.571
    299. 

  299. KNN cohens kappa score: 0.554
    300. 

  300. 

  301. 

  302. ====== Step 2/5 =======
    303. 

  303. -> Shuffling data
    304. 

  304. -> Spliting data to slices
    305. 

  305. 

  306. ------ Step 2/5: Slice 1/5 -------
    307. 

  307. -> Reset the GAN
    308. 

  308. -> Train generator for synthetic samples
    309. 

  309. -> create 1117 synthetic samples
    310. 

  310. -> retrain GAN for predict
    311. 

  311. Epoch 1/10
    312. 

  312. 
  1/116 [..............................] - ETA: 20s - loss: 0.0392
 36/116 [========>.....................] - ETA: 0s - loss: 0.0744 
 72/116 [=================>............] - ETA: 0s - loss: 0.0822
108/116 [==========================>...] - ETA: 0s - loss: 0.0899
116/116 [==============================] - 0s 1ms/step - loss: 0.0890
    313. 

  313. Epoch 2/10
    314. 

  314. 
  1/116 [..............................] - ETA: 0s - loss: 0.0466
 37/116 [========>.....................] - ETA: 0s - loss: 0.0854
 72/116 [=================>............] - ETA: 0s - loss: 0.0898
109/116 [===========================>..] - ETA: 0s - loss: 0.0882
116/116 [==============================] - 0s 1ms/step - loss: 0.0875
    315. 

  315. Epoch 3/10
    316. 

  316. 
  1/116 [..............................] - ETA: 0s - loss: 0.1701
 35/116 [========>.....................] - ETA: 0s - loss: 0.0893
 70/116 [=================>............] - ETA: 0s - loss: 0.0907
104/116 [=========================>....] - ETA: 0s - loss: 0.0851
116/116 [==============================] - 0s 1ms/step - loss: 0.0860
    317. 

  317. Epoch 4/10
    318. 

  318. 
  1/116 [..............................] - ETA: 0s - loss: 0.0119
 34/116 [=======>......................] - ETA: 0s - loss: 0.0858
 70/116 [=================>............] - ETA: 0s - loss: 0.0928
105/116 [==========================>...] - ETA: 0s - loss: 0.0845
116/116 [==============================] - 0s 1ms/step - loss: 0.0838
    319. 

  319. Epoch 5/10
    320. 

  320. 
  1/116 [..............................] - ETA: 0s - loss: 0.0175
 32/116 [=======>......................] - ETA: 0s - loss: 0.1113
 64/116 [===============>..............] - ETA: 0s - loss: 0.0949
 99/116 [========================>.....] - ETA: 0s - loss: 0.0911
116/116 [==============================] - 0s 2ms/step - loss: 0.0844
    321. 

  321. Epoch 6/10
    322. 

  322. 
  1/116 [..............................] - ETA: 0s - loss: 0.0343
 38/116 [========>.....................] - ETA: 0s - loss: 0.0970
 74/116 [==================>...........] - ETA: 0s - loss: 0.0860
110/116 [===========================>..] - ETA: 0s - loss: 0.0839
116/116 [==============================] - 0s 1ms/step - loss: 0.0818
    323. 

  323. Epoch 7/10
    324. 

  324. 
  1/116 [..............................] - ETA: 0s - loss: 0.1001
 36/116 [========>.....................] - ETA: 0s - loss: 0.0797
 70/116 [=================>............] - ETA: 0s - loss: 0.0793
105/116 [==========================>...] - ETA: 0s - loss: 0.0806
116/116 [==============================] - 0s 1ms/step - loss: 0.0800
    325. 

  325. Epoch 8/10
    326. 

  326. 
  1/116 [..............................] - ETA: 0s - loss: 0.4191
 35/116 [========>.....................] - ETA: 0s - loss: 0.0779
 69/116 [================>.............] - ETA: 0s - loss: 0.0832
102/116 [=========================>....] - ETA: 0s - loss: 0.0805
116/116 [==============================] - 0s 1ms/step - loss: 0.0784
    327. 

  327. Epoch 9/10
    328. 

  328. 
  1/116 [..............................] - ETA: 0s - loss: 0.0181
 35/116 [========>.....................] - ETA: 0s - loss: 0.1065
 72/116 [=================>............] - ETA: 0s - loss: 0.0937
107/116 [==========================>...] - ETA: 0s - loss: 0.0795
116/116 [==============================] - 0s 1ms/step - loss: 0.0782
    329. 

  329. Epoch 10/10
    330. 

  330. 
  1/116 [..............................] - ETA: 0s - loss: 0.0344
 34/116 [=======>......................] - ETA: 0s - loss: 0.0835
 71/116 [=================>............] - ETA: 0s - loss: 0.0822
108/116 [==========================>...] - ETA: 0s - loss: 0.0782
116/116 [==============================] - 0s 1ms/step - loss: 0.0774
    331. 

  331. -> test with GAN.predict
    332. 

  332. GAN tn, fp: 276, 12
    333. 

  333. GAN fn, tp: 1, 8
    334. 

  334. GAN f1 score: 0.552
    335. 

  335. GAN cohens kappa score: 0.532
    336. 

  336. 

  337. -> test with 'LR'
    338. 

  338. LR tn, fp: 274, 14
    339. 

  339. LR fn, tp: 0, 9
    340. 

  340. LR f1 score: 0.562
    341. 

  341. LR cohens kappa score: 0.543
    342. 

  342. LR average precision score: 0.655
    343. 

  343. 

  344. -> test with 'RF'
    345. 

  345. RF tn, fp: 287, 1
    346. 

  346. RF fn, tp: 1, 8
    347. 

  347. RF f1 score: 0.889
    348. 

  348. RF cohens kappa score: 0.885
    349. 

  349. 

  350. -> test with 'GB'
    351. 

  351. GB tn, fp: 286, 2
    352. 

  352. GB fn, tp: 1, 8
    353. 

  353. GB f1 score: 0.842
    354. 

  354. GB cohens kappa score: 0.837
    355. 

  355. 

  356. -> test with 'KNN'
    357. 

  357. KNN tn, fp: 282, 6
    358. 

  358. KNN fn, tp: 0, 9
    359. 

  359. KNN f1 score: 0.750
    360. 

  360. KNN cohens kappa score: 0.740
    361. 

  361. 

  362. 

  363. ------ Step 2/5: Slice 2/5 -------
    364. 

  364. -> Reset the GAN
    365. 

  365. -> Train generator for synthetic samples
    366. 

  366. -> create 1117 synthetic samples
    367. 

  367. -> retrain GAN for predict
    368. 

  368. Epoch 1/10
    369. 

  369. 
  1/116 [..............................] - ETA: 18s - loss: 0.0272
 37/116 [========>.....................] - ETA: 0s - loss: 0.0671 
 73/116 [=================>............] - ETA: 0s - loss: 0.0684
110/116 [===========================>..] - ETA: 0s - loss: 0.0775
116/116 [==============================] - 0s 1ms/step - loss: 0.0776
    370. 

  370. Epoch 2/10
    371. 

  371. 
  1/116 [..............................] - ETA: 0s - loss: 0.0180
 37/116 [========>.....................] - ETA: 0s - loss: 0.0812
 75/116 [==================>...........] - ETA: 0s - loss: 0.0765
113/116 [============================>.] - ETA: 0s - loss: 0.0742
116/116 [==============================] - 0s 1ms/step - loss: 0.0746
    372. 

  372. Epoch 3/10
    373. 

  373. 
  1/116 [..............................] - ETA: 0s - loss: 0.0417
 39/116 [=========>....................] - ETA: 0s - loss: 0.0851
 77/116 [==================>...........] - ETA: 0s - loss: 0.0800
114/116 [============================>.] - ETA: 0s - loss: 0.0747
116/116 [==============================] - 0s 1ms/step - loss: 0.0751
    374. 

  374. Epoch 4/10
    375. 

  375. 
  1/116 [..............................] - ETA: 0s - loss: 0.0207
 38/116 [========>.....................] - ETA: 0s - loss: 0.0847
 76/116 [==================>...........] - ETA: 0s - loss: 0.0816
114/116 [============================>.] - ETA: 0s - loss: 0.0727
116/116 [==============================] - 0s 1ms/step - loss: 0.0725
    376. 

  376. Epoch 5/10
    377. 

  377. 
  1/116 [..............................] - ETA: 0s - loss: 0.2582
 38/116 [========>.....................] - ETA: 0s - loss: 0.0718
 75/116 [==================>...........] - ETA: 0s - loss: 0.0723
111/116 [===========================>..] - ETA: 0s - loss: 0.0705
116/116 [==============================] - 0s 1ms/step - loss: 0.0695
    378. 

  378. Epoch 6/10
    379. 

  379. 
  1/116 [..............................] - ETA: 0s - loss: 0.0506
 40/116 [=========>....................] - ETA: 0s - loss: 0.0863
 79/116 [===================>..........] - ETA: 0s - loss: 0.0660
116/116 [==============================] - ETA: 0s - loss: 0.0678
116/116 [==============================] - 0s 1ms/step - loss: 0.0678
    380. 

  380. Epoch 7/10
    381. 

  381. 
  1/116 [..............................] - ETA: 0s - loss: 0.0269
 38/116 [========>.....................] - ETA: 0s - loss: 0.0775
 74/116 [==================>...........] - ETA: 0s - loss: 0.0759
110/116 [===========================>..] - ETA: 0s - loss: 0.0653
116/116 [==============================] - 0s 1ms/step - loss: 0.0643
    382. 

  382. Epoch 8/10
    383. 

  383. 
  1/116 [..............................] - ETA: 0s - loss: 0.0177
 32/116 [=======>......................] - ETA: 0s - loss: 0.0840
 61/116 [==============>...............] - ETA: 0s - loss: 0.0761
 92/116 [======================>.......] - ETA: 0s - loss: 0.0666
116/116 [==============================] - 0s 2ms/step - loss: 0.0629
    384. 

  384. Epoch 9/10
    385. 

  385. 
  1/116 [..............................] - ETA: 0s - loss: 0.0890
 39/116 [=========>....................] - ETA: 0s - loss: 0.0686
 72/116 [=================>............] - ETA: 0s - loss: 0.0662
108/116 [==========================>...] - ETA: 0s - loss: 0.0601
116/116 [==============================] - 0s 1ms/step - loss: 0.0625
    386. 

  386. Epoch 10/10
    387. 

  387. 
  1/116 [..............................] - ETA: 0s - loss: 0.0261
 37/116 [========>.....................] - ETA: 0s - loss: 0.0568
 75/116 [==================>...........] - ETA: 0s - loss: 0.0595
113/116 [============================>.] - ETA: 0s - loss: 0.0604
116/116 [==============================] - 0s 1ms/step - loss: 0.0603
    388. 

  388. -> test with GAN.predict
    389. 

  389. GAN tn, fp: 277, 11
    390. 

  390. GAN fn, tp: 2, 7
    391. 

  391. GAN f1 score: 0.519
    392. 

  392. GAN cohens kappa score: 0.498
    393. 

  393. 

  394. -> test with 'LR'
    395. 

  395. LR tn, fp: 271, 17
    396. 

  396. LR fn, tp: 1, 8
    397. 

  397. LR f1 score: 0.471
    398. 

  398. LR cohens kappa score: 0.446
    399. 

  399. LR average precision score: 0.343
    400. 

  400. 

  401. -> test with 'RF'
    402. 

  402. RF tn, fp: 283, 5
    403. 

  403. RF fn, tp: 6, 3
    404. 

  404. RF f1 score: 0.353
    405. 

  405. RF cohens kappa score: 0.334
    406. 

  406. 

  407. -> test with 'GB'
    408. 

  408. GB tn, fp: 283, 5
    409. 

  409. GB fn, tp: 6, 3
    410. 

  410. GB f1 score: 0.353
    411. 

  411. GB cohens kappa score: 0.334
    412. 

  412. 

  413. -> test with 'KNN'
    414. 

  414. KNN tn, fp: 274, 14
    415. 

  415. KNN fn, tp: 0, 9
    416. 

  416. KNN f1 score: 0.562
    417. 

  417. KNN cohens kappa score: 0.543
    418. 

  418. 

  419. 

  420. ------ Step 2/5: Slice 3/5 -------
    421. 

  421. -> Reset the GAN
    422. 

  422. -> Train generator for synthetic samples
    423. 

  423. -> create 1117 synthetic samples
    424. 

  424. -> retrain GAN for predict
    425. 

  425. Epoch 1/10
    426. 

  426. 
  1/116 [..............................] - ETA: 18s - loss: 0.0376
 39/116 [=========>....................] - ETA: 0s - loss: 0.0903 
 71/116 [=================>............] - ETA: 0s - loss: 0.0833
111/116 [===========================>..] - ETA: 0s - loss: 0.0873
116/116 [==============================] - 0s 1ms/step - loss: 0.0859
    427. 

  427. Epoch 2/10
    428. 

  428. 
  1/116 [..............................] - ETA: 0s - loss: 0.0903
 39/116 [=========>....................] - ETA: 0s - loss: 0.0659
 77/116 [==================>...........] - ETA: 0s - loss: 0.0764
115/116 [============================>.] - ETA: 0s - loss: 0.0848
116/116 [==============================] - 0s 1ms/step - loss: 0.0847
    429. 

  429. Epoch 3/10
    430. 

  430. 
  1/116 [..............................] - ETA: 0s - loss: 0.0585
 41/116 [=========>....................] - ETA: 0s - loss: 0.0868
 80/116 [===================>..........] - ETA: 0s - loss: 0.0864
116/116 [==============================] - 0s 1ms/step - loss: 0.0830
    431. 

  431. Epoch 4/10
    432. 

  432. 
  1/116 [..............................] - ETA: 0s - loss: 0.0267
 41/116 [=========>....................] - ETA: 0s - loss: 0.1050
 78/116 [===================>..........] - ETA: 0s - loss: 0.0880
116/116 [==============================] - 0s 1ms/step - loss: 0.0822
    433. 

  433. Epoch 5/10
    434. 

  434. 
  1/116 [..............................] - ETA: 0s - loss: 0.1661
 40/116 [=========>....................] - ETA: 0s - loss: 0.0836
 83/116 [====================>.........] - ETA: 0s - loss: 0.0737
116/116 [==============================] - 0s 1ms/step - loss: 0.0811
    435. 

  435. Epoch 6/10
    436. 

  436. 
  1/116 [..............................] - ETA: 0s - loss: 0.0286
 41/116 [=========>....................] - ETA: 0s - loss: 0.0866
 81/116 [===================>..........] - ETA: 0s - loss: 0.0873
116/116 [==============================] - 0s 1ms/step - loss: 0.0816
    437. 

  437. Epoch 7/10
    438. 

  438. 
  1/116 [..............................] - ETA: 0s - loss: 0.0183
 40/116 [=========>....................] - ETA: 0s - loss: 0.0824
 77/116 [==================>...........] - ETA: 0s - loss: 0.0830
116/116 [==============================] - ETA: 0s - loss: 0.0802
116/116 [==============================] - 0s 1ms/step - loss: 0.0802
    439. 

  439. Epoch 8/10
    440. 

  440. 
  1/116 [..............................] - ETA: 0s - loss: 0.0367
 38/116 [========>.....................] - ETA: 0s - loss: 0.0837
 77/116 [==================>...........] - ETA: 0s - loss: 0.0817
115/116 [============================>.] - ETA: 0s - loss: 0.0799
116/116 [==============================] - 0s 1ms/step - loss: 0.0798
    441. 

  441. Epoch 9/10
    442. 

  442. 
  1/116 [..............................] - ETA: 0s - loss: 0.0486
 40/116 [=========>....................] - ETA: 0s - loss: 0.0758
 79/116 [===================>..........] - ETA: 0s - loss: 0.0805
116/116 [==============================] - 0s 1ms/step - loss: 0.0798
    443. 

  443. Epoch 10/10
    444. 

  444. 
  1/116 [..............................] - ETA: 0s - loss: 0.1113
 40/116 [=========>....................] - ETA: 0s - loss: 0.0673
 79/116 [===================>..........] - ETA: 0s - loss: 0.0652
116/116 [==============================] - 0s 1ms/step - loss: 0.0762
    445. 

  445. -> test with GAN.predict
    446. 

  446. GAN tn, fp: 281, 7
    447. 

  447. GAN fn, tp: 1, 8
    448. 

  448. GAN f1 score: 0.667
    449. 

  449. GAN cohens kappa score: 0.654
    450. 

  450. 

  451. -> test with 'LR'
    452. 

  452. LR tn, fp: 281, 7
    453. 

  453. LR fn, tp: 1, 8
    454. 

  454. LR f1 score: 0.667
    455. 

  455. LR cohens kappa score: 0.654
    456. 

  456. LR average precision score: 0.741
    457. 

  457. 

  458. -> test with 'RF'
    459. 

  459. RF tn, fp: 288, 0
    460. 

  460. RF fn, tp: 2, 7
    461. 

  461. RF f1 score: 0.875
    462. 

  462. RF cohens kappa score: 0.872
    463. 

  463. 

  464. -> test with 'GB'
    465. 

  465. GB tn, fp: 286, 2
    466. 

  466. GB fn, tp: 1, 8
    467. 

  467. GB f1 score: 0.842
    468. 

  468. GB cohens kappa score: 0.837
    469. 

  469. 

  470. -> test with 'KNN'
    471. 

  471. KNN tn, fp: 281, 7
    472. 

  472. KNN fn, tp: 1, 8
    473. 

  473. KNN f1 score: 0.667
    474. 

  474. KNN cohens kappa score: 0.654
    475. 

  475. 

  476. 

  477. ------ Step 2/5: Slice 4/5 -------
    478. 

  478. -> Reset the GAN
    479. 

  479. -> Train generator for synthetic samples
    480. 

  480. -> create 1117 synthetic samples
    481. 

  481. -> retrain GAN for predict
    482. 

  482. Epoch 1/10
    483. 

  483. 
  1/116 [..............................] - ETA: 18s - loss: 0.0547
 38/116 [========>.....................] - ETA: 0s - loss: 0.0942 
 76/116 [==================>...........] - ETA: 0s - loss: 0.0856
111/116 [===========================>..] - ETA: 0s - loss: 0.0936
116/116 [==============================] - 0s 1ms/step - loss: 0.0926
    484. 

  484. Epoch 2/10
    485. 

  485. 
  1/116 [..............................] - ETA: 0s - loss: 0.0186
 36/116 [========>.....................] - ETA: 0s - loss: 0.0833
 75/116 [==================>...........] - ETA: 0s - loss: 0.0981
113/116 [============================>.] - ETA: 0s - loss: 0.0916
116/116 [==============================] - 0s 1ms/step - loss: 0.0908
    486. 

  486. Epoch 3/10
    487. 

  487. 
  1/116 [..............................] - ETA: 0s - loss: 0.1832
 37/116 [========>.....................] - ETA: 0s - loss: 0.0733
 68/116 [================>.............] - ETA: 0s - loss: 0.0850
 98/116 [========================>.....] - ETA: 0s - loss: 0.0874
116/116 [==============================] - 0s 2ms/step - loss: 0.0895
    488. 

  488. Epoch 4/10
    489. 

  489. 
  1/116 [..............................] - ETA: 0s - loss: 0.1133
 39/116 [=========>....................] - ETA: 0s - loss: 0.0865
 77/116 [==================>...........] - ETA: 0s - loss: 0.0861
115/116 [============================>.] - ETA: 0s - loss: 0.0882
116/116 [==============================] - 0s 1ms/step - loss: 0.0888
    490. 

  490. Epoch 5/10
    491. 

  491. 
  1/116 [..............................] - ETA: 0s - loss: 0.0572
 40/116 [=========>....................] - ETA: 0s - loss: 0.1102
 78/116 [===================>..........] - ETA: 0s - loss: 0.0955
116/116 [==============================] - 0s 1ms/step - loss: 0.0873
    492. 

  492. Epoch 6/10
    493. 

  493. 
  1/116 [..............................] - ETA: 0s - loss: 0.0906
 37/116 [========>.....................] - ETA: 0s - loss: 0.0992
 74/116 [==================>...........] - ETA: 0s - loss: 0.0847
110/116 [===========================>..] - ETA: 0s - loss: 0.0886
116/116 [==============================] - 0s 1ms/step - loss: 0.0880
    494. 

  494. Epoch 7/10
    495. 

  495. 
  1/116 [..............................] - ETA: 0s - loss: 0.2560
 40/116 [=========>....................] - ETA: 0s - loss: 0.0884
 80/116 [===================>..........] - ETA: 0s - loss: 0.0831
116/116 [==============================] - 0s 1ms/step - loss: 0.0846
    496. 

  496. Epoch 8/10
    497. 

  497. 
  1/116 [..............................] - ETA: 0s - loss: 0.0204
 39/116 [=========>....................] - ETA: 0s - loss: 0.0795
 77/116 [==================>...........] - ETA: 0s - loss: 0.0780
115/116 [============================>.] - ETA: 0s - loss: 0.0839
116/116 [==============================] - 0s 1ms/step - loss: 0.0846
    498. 

  498. Epoch 9/10
    499. 

  499. 
  1/116 [..............................] - ETA: 0s - loss: 0.0720
 40/116 [=========>....................] - ETA: 0s - loss: 0.0806
 78/116 [===================>..........] - ETA: 0s - loss: 0.0831
114/116 [============================>.] - ETA: 0s - loss: 0.0843
116/116 [==============================] - 0s 1ms/step - loss: 0.0836
    500. 

  500. Epoch 10/10
    501. 

  501. 
  1/116 [..............................] - ETA: 0s - loss: 0.0262
 39/116 [=========>....................] - ETA: 0s - loss: 0.0897
 77/116 [==================>...........] - ETA: 0s - loss: 0.0850
116/116 [==============================] - 0s 1ms/step - loss: 0.0831
    502. 

  502. -> test with GAN.predict
    503. 

  503. GAN tn, fp: 274, 14
    504. 

  504. GAN fn, tp: 0, 9
    505. 

  505. GAN f1 score: 0.562
    506. 

  506. GAN cohens kappa score: 0.543
    507. 

  507. 

  508. -> test with 'LR'
    509. 

  509. LR tn, fp: 272, 16
    510. 

  510. LR fn, tp: 0, 9
    511. 

  511. LR f1 score: 0.529
    512. 

  512. LR cohens kappa score: 0.507
    513. 

  513. LR average precision score: 0.878
    514. 

  514. 

  515. -> test with 'RF'
    516. 

  516. RF tn, fp: 286, 2
    517. 

  517. RF fn, tp: 3, 6
    518. 

  518. RF f1 score: 0.706
    519. 

  519. RF cohens kappa score: 0.697
    520. 

  520. 

  521. -> test with 'GB'
    522. 

  522. GB tn, fp: 285, 3
    523. 

  523. GB fn, tp: 2, 7
    524. 

  524. GB f1 score: 0.737
    525. 

  525. GB cohens kappa score: 0.728
    526. 

  526. 

  527. -> test with 'KNN'
    528. 

  528. KNN tn, fp: 279, 9
    529. 

  529. KNN fn, tp: 0, 9
    530. 

  530. KNN f1 score: 0.667
    531. 

  531. KNN cohens kappa score: 0.653
    532. 

  532. 

  533. 

  534. ------ Step 2/5: Slice 5/5 -------
    535. 

  535. -> Reset the GAN
    536. 

  536. -> Train generator for synthetic samples
    537. 

  537. -> create 1116 synthetic samples
    538. 

  538. -> retrain GAN for predict
    539. 

  539. Epoch 1/10
    540. 

  540. 
  1/116 [..............................] - ETA: 17s - loss: 0.2000
 39/116 [=========>....................] - ETA: 0s - loss: 0.0774 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0745
116/116 [==============================] - ETA: 0s - loss: 0.0723
116/116 [==============================] - 0s 1ms/step - loss: 0.0723
    541. 

  541. Epoch 2/10
    542. 

  542. 
  1/116 [..............................] - ETA: 0s - loss: 0.0813
 39/116 [=========>....................] - ETA: 0s - loss: 0.0649
 79/116 [===================>..........] - ETA: 0s - loss: 0.0687
116/116 [==============================] - 0s 1ms/step - loss: 0.0727
    543. 

  543. Epoch 3/10
    544. 

  544. 
  1/116 [..............................] - ETA: 0s - loss: 0.0896
 40/116 [=========>....................] - ETA: 0s - loss: 0.0511
 78/116 [===================>..........] - ETA: 0s - loss: 0.0595
116/116 [==============================] - ETA: 0s - loss: 0.0707
116/116 [==============================] - 0s 1ms/step - loss: 0.0707
    545. 

  545. Epoch 4/10
    546. 

  546. 
  1/116 [..............................] - ETA: 0s - loss: 0.1048
 40/116 [=========>....................] - ETA: 0s - loss: 0.0811
 80/116 [===================>..........] - ETA: 0s - loss: 0.0757
116/116 [==============================] - 0s 1ms/step - loss: 0.0687
    547. 

  547. Epoch 5/10
    548. 

  548. 
  1/116 [..............................] - ETA: 0s - loss: 0.0550
 39/116 [=========>....................] - ETA: 0s - loss: 0.0615
 76/116 [==================>...........] - ETA: 0s - loss: 0.0614
116/116 [==============================] - ETA: 0s - loss: 0.0681
116/116 [==============================] - 0s 1ms/step - loss: 0.0681
    549. 

  549. Epoch 6/10
    550. 

  550. 
  1/116 [..............................] - ETA: 0s - loss: 0.0176
 39/116 [=========>....................] - ETA: 0s - loss: 0.0748
 77/116 [==================>...........] - ETA: 0s - loss: 0.0656
115/116 [============================>.] - ETA: 0s - loss: 0.0662
116/116 [==============================] - 0s 1ms/step - loss: 0.0679
    551. 

  551. Epoch 7/10
    552. 

  552. 
  1/116 [..............................] - ETA: 0s - loss: 0.1177
 38/116 [========>.....................] - ETA: 0s - loss: 0.0774
 78/116 [===================>..........] - ETA: 0s - loss: 0.0735
116/116 [==============================] - ETA: 0s - loss: 0.0671
116/116 [==============================] - 0s 1ms/step - loss: 0.0671
    553. 

  553. Epoch 8/10
    554. 

  554. 
  1/116 [..............................] - ETA: 0s - loss: 0.0177
 40/116 [=========>....................] - ETA: 0s - loss: 0.0650
 79/116 [===================>..........] - ETA: 0s - loss: 0.0612
116/116 [==============================] - 0s 1ms/step - loss: 0.0676
    555. 

  555. Epoch 9/10
    556. 

  556. 
  1/116 [..............................] - ETA: 0s - loss: 0.1616
 40/116 [=========>....................] - ETA: 0s - loss: 0.0694
 78/116 [===================>..........] - ETA: 0s - loss: 0.0693
112/116 [===========================>..] - ETA: 0s - loss: 0.0662
116/116 [==============================] - 0s 1ms/step - loss: 0.0656
    557. 

  557. Epoch 10/10
    558. 

  558. 
  1/116 [..............................] - ETA: 0s - loss: 0.2012
 37/116 [========>.....................] - ETA: 0s - loss: 0.0522
 69/116 [================>.............] - ETA: 0s - loss: 0.0568
108/116 [==========================>...] - ETA: 0s - loss: 0.0672
116/116 [==============================] - 0s 1ms/step - loss: 0.0647
    559. 

  559. -> test with GAN.predict
    560. 

  560. GAN tn, fp: 278, 10
    561. 

  561. GAN fn, tp: 0, 8
    562. 

  562. GAN f1 score: 0.615
    563. 

  563. GAN cohens kappa score: 0.600
    564. 

  564. 

  565. -> test with 'LR'
    566. 

  566. LR tn, fp: 278, 10
    567. 

  567. LR fn, tp: 0, 8
    568. 

  568. LR f1 score: 0.615
    569. 

  569. LR cohens kappa score: 0.600
    570. 

  570. LR average precision score: 0.614
    571. 

  571. 

  572. -> test with 'RF'
    573. 

  573. RF tn, fp: 287, 1
    574. 

  574. RF fn, tp: 6, 2
    575. 

  575. RF f1 score: 0.364
    576. 

  576. RF cohens kappa score: 0.354
    577. 

  577. 

  578. -> test with 'GB'
    579. 

  579. GB tn, fp: 286, 2
    580. 

  580. GB fn, tp: 5, 3
    581. 

  581. GB f1 score: 0.462
    582. 

  582. GB cohens kappa score: 0.450
    583. 

  583. 

  584. -> test with 'KNN'
    585. 

  585. KNN tn, fp: 282, 6
    586. 

  586. KNN fn, tp: 0, 8
    587. 

  587. KNN f1 score: 0.727
    588. 

  588. KNN cohens kappa score: 0.718
    589. 

  589. 

  590. 

  591. ====== Step 3/5 =======
    592. 

  592. -> Shuffling data
    593. 

  593. -> Spliting data to slices
    594. 

  594. 

  595. ------ Step 3/5: Slice 1/5 -------
    596. 

  596. -> Reset the GAN
    597. 

  597. -> Train generator for synthetic samples
    598. 

  598. -> create 1117 synthetic samples
    599. 

  599. -> retrain GAN for predict
    600. 

  600. Epoch 1/10
    601. 

  601. 
  1/116 [..............................] - ETA: 18s - loss: 0.0281
 42/116 [=========>....................] - ETA: 0s - loss: 0.0725 
 79/116 [===================>..........] - ETA: 0s - loss: 0.0837
115/116 [============================>.] - ETA: 0s - loss: 0.0821
116/116 [==============================] - 0s 1ms/step - loss: 0.0823
    602. 

  602. Epoch 2/10
    603. 

  603. 
  1/116 [..............................] - ETA: 0s - loss: 0.0394
 38/116 [========>.....................] - ETA: 0s - loss: 0.0607
 75/116 [==================>...........] - ETA: 0s - loss: 0.0768
113/116 [============================>.] - ETA: 0s - loss: 0.0829
116/116 [==============================] - 0s 1ms/step - loss: 0.0816
    604. 

  604. Epoch 3/10
    605. 

  605. 
  1/116 [..............................] - ETA: 0s - loss: 0.0179
 42/116 [=========>....................] - ETA: 0s - loss: 0.0730
 83/116 [====================>.........] - ETA: 0s - loss: 0.0810
116/116 [==============================] - 0s 1ms/step - loss: 0.0818
    606. 

  606. Epoch 4/10
    607. 

  607. 
  1/116 [..............................] - ETA: 0s - loss: 0.0763
 39/116 [=========>....................] - ETA: 0s - loss: 0.0706
 76/116 [==================>...........] - ETA: 0s - loss: 0.0797
114/116 [============================>.] - ETA: 0s - loss: 0.0796
116/116 [==============================] - 0s 1ms/step - loss: 0.0790
    608. 

  608. Epoch 5/10
    609. 

  609. 
  1/116 [..............................] - ETA: 0s - loss: 0.0248
 40/116 [=========>....................] - ETA: 0s - loss: 0.0666
 77/116 [==================>...........] - ETA: 0s - loss: 0.0739
115/116 [============================>.] - ETA: 0s - loss: 0.0779
116/116 [==============================] - 0s 1ms/step - loss: 0.0778
    610. 

  610. Epoch 6/10
    611. 

  611. 
  1/116 [..............................] - ETA: 0s - loss: 0.2806
 36/116 [========>.....................] - ETA: 0s - loss: 0.0633
 72/116 [=================>............] - ETA: 0s - loss: 0.0727
106/116 [==========================>...] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0756
    612. 

  612. Epoch 7/10
    613. 

  613. 
  1/116 [..............................] - ETA: 0s - loss: 0.2936
 39/116 [=========>....................] - ETA: 0s - loss: 0.0694
 76/116 [==================>...........] - ETA: 0s - loss: 0.0760
115/116 [============================>.] - ETA: 0s - loss: 0.0747
116/116 [==============================] - 0s 1ms/step - loss: 0.0765
    614. 

  614. Epoch 8/10
    615. 

  615. 
  1/116 [..............................] - ETA: 0s - loss: 0.1070
 40/116 [=========>....................] - ETA: 0s - loss: 0.0720
 77/116 [==================>...........] - ETA: 0s - loss: 0.0729
115/116 [============================>.] - ETA: 0s - loss: 0.0743
116/116 [==============================] - 0s 1ms/step - loss: 0.0742
    616. 

  616. Epoch 9/10
    617. 

  617. 
  1/116 [..............................] - ETA: 0s - loss: 0.0266
 41/116 [=========>....................] - ETA: 0s - loss: 0.0622
 81/116 [===================>..........] - ETA: 0s - loss: 0.0695
116/116 [==============================] - 0s 1ms/step - loss: 0.0733
    618. 

  618. Epoch 10/10
    619. 

  619. 
  1/116 [..............................] - ETA: 0s - loss: 0.1200
 39/116 [=========>....................] - ETA: 0s - loss: 0.0721
 77/116 [==================>...........] - ETA: 0s - loss: 0.0668
114/116 [============================>.] - ETA: 0s - loss: 0.0727
116/116 [==============================] - 0s 1ms/step - loss: 0.0723
    620. 

  620. -> test with GAN.predict
    621. 

  621. GAN tn, fp: 272, 16
    622. 

  622. GAN fn, tp: 1, 8
    623. 

  623. GAN f1 score: 0.485
    624. 

  624. GAN cohens kappa score: 0.461
    625. 

  625. 

  626. -> test with 'LR'
    627. 

  627. LR tn, fp: 272, 16
    628. 

  628. LR fn, tp: 0, 9
    629. 

  629. LR f1 score: 0.529
    630. 

  630. LR cohens kappa score: 0.507
    631. 

  631. LR average precision score: 0.673
    632. 

  632. 

  633. -> test with 'RF'
    634. 

  634. RF tn, fp: 286, 2
    635. 

  635. RF fn, tp: 3, 6
    636. 

  636. RF f1 score: 0.706
    637. 

  637. RF cohens kappa score: 0.697
    638. 

  638. 

  639. -> test with 'GB'
    640. 

  640. GB tn, fp: 286, 2
    641. 

  641. GB fn, tp: 3, 6
    642. 

  642. GB f1 score: 0.706
    643. 

  643. GB cohens kappa score: 0.697
    644. 

  644. 

  645. -> test with 'KNN'
    646. 

  646. KNN tn, fp: 274, 14
    647. 

  647. KNN fn, tp: 0, 9
    648. 

  648. KNN f1 score: 0.562
    649. 

  649. KNN cohens kappa score: 0.543
    650. 

  650. 

  651. 

  652. ------ Step 3/5: Slice 2/5 -------
    653. 

  653. -> Reset the GAN
    654. 

  654. -> Train generator for synthetic samples
    655. 

  655. -> create 1117 synthetic samples
    656. 

  656. -> retrain GAN for predict
    657. 

  657. Epoch 1/10
    658. 

  658. 
  1/116 [..............................] - ETA: 21s - loss: 0.2115
 37/116 [========>.....................] - ETA: 0s - loss: 0.0821 
 77/116 [==================>...........] - ETA: 0s - loss: 0.0805
115/116 [============================>.] - ETA: 0s - loss: 0.0755
116/116 [==============================] - 0s 1ms/step - loss: 0.0756
    659. 

  659. Epoch 2/10
    660. 

  660. 
  1/116 [..............................] - ETA: 0s - loss: 0.0131
 39/116 [=========>....................] - ETA: 0s - loss: 0.0744
 78/116 [===================>..........] - ETA: 0s - loss: 0.0758
115/116 [============================>.] - ETA: 0s - loss: 0.0756
116/116 [==============================] - 0s 1ms/step - loss: 0.0756
    661. 

  661. Epoch 3/10
    662. 

  662. 
  1/116 [..............................] - ETA: 0s - loss: 0.0428
 36/116 [========>.....................] - ETA: 0s - loss: 0.0708
 73/116 [=================>............] - ETA: 0s - loss: 0.0764
111/116 [===========================>..] - ETA: 0s - loss: 0.0744
116/116 [==============================] - 0s 1ms/step - loss: 0.0730
    663. 

  663. Epoch 4/10
    664. 

  664. 
  1/116 [..............................] - ETA: 0s - loss: 0.0177
 40/116 [=========>....................] - ETA: 0s - loss: 0.0682
 79/116 [===================>..........] - ETA: 0s - loss: 0.0701
116/116 [==============================] - 0s 1ms/step - loss: 0.0720
    665. 

  665. Epoch 5/10
    666. 

  666. 
  1/116 [..............................] - ETA: 0s - loss: 0.0129
 39/116 [=========>....................] - ETA: 0s - loss: 0.0807
 75/116 [==================>...........] - ETA: 0s - loss: 0.0786
113/116 [============================>.] - ETA: 0s - loss: 0.0725
116/116 [==============================] - 0s 1ms/step - loss: 0.0714
    667. 

  667. Epoch 6/10
    668. 

  668. 
  1/116 [..............................] - ETA: 0s - loss: 0.1297
 38/116 [========>.....................] - ETA: 0s - loss: 0.0783
 73/116 [=================>............] - ETA: 0s - loss: 0.0632
104/116 [=========================>....] - ETA: 0s - loss: 0.0671
116/116 [==============================] - 0s 1ms/step - loss: 0.0701
    669. 

  669. Epoch 7/10
    670. 

  670. 
  1/116 [..............................] - ETA: 0s - loss: 0.1273
 34/116 [=======>......................] - ETA: 0s - loss: 0.0738
 69/116 [================>.............] - ETA: 0s - loss: 0.0678
106/116 [==========================>...] - ETA: 0s - loss: 0.0718
116/116 [==============================] - 0s 1ms/step - loss: 0.0693
    671. 

  671. Epoch 8/10
    672. 

  672. 
  1/116 [..............................] - ETA: 0s - loss: 0.0163
 40/116 [=========>....................] - ETA: 0s - loss: 0.0600
 79/116 [===================>..........] - ETA: 0s - loss: 0.0676
116/116 [==============================] - 0s 1ms/step - loss: 0.0684
    673. 

  673. Epoch 9/10
    674. 

  674. 
  1/116 [..............................] - ETA: 0s - loss: 0.0132
 39/116 [=========>....................] - ETA: 0s - loss: 0.0563
 78/116 [===================>..........] - ETA: 0s - loss: 0.0623
116/116 [==============================] - ETA: 0s - loss: 0.0664
116/116 [==============================] - 0s 1ms/step - loss: 0.0664
    675. 

  675. Epoch 10/10
    676. 

  676. 
  1/116 [..............................] - ETA: 0s - loss: 0.0656
 39/116 [=========>....................] - ETA: 0s - loss: 0.0829
 78/116 [===================>..........] - ETA: 0s - loss: 0.0690
116/116 [==============================] - ETA: 0s - loss: 0.0665
116/116 [==============================] - 0s 1ms/step - loss: 0.0665
    677. 

  677. -> test with GAN.predict
    678. 

  678. GAN tn, fp: 274, 14
    679. 

  679. GAN fn, tp: 0, 9
    680. 

  680. GAN f1 score: 0.562
    681. 

  681. GAN cohens kappa score: 0.543
    682. 

  682. 

  683. -> test with 'LR'
    684. 

  684. LR tn, fp: 274, 14
    685. 

  685. LR fn, tp: 0, 9
    686. 

  686. LR f1 score: 0.562
    687. 

  687. LR cohens kappa score: 0.543
    688. 

  688. LR average precision score: 0.708
    689. 

  689. 

  690. -> test with 'RF'
    691. 

  691. RF tn, fp: 287, 1
    692. 

  692. RF fn, tp: 3, 6
    693. 

  693. RF f1 score: 0.750
    694. 

  694. RF cohens kappa score: 0.743
    695. 

  695. 

  696. -> test with 'GB'
    697. 

  697. GB tn, fp: 286, 2
    698. 

  698. GB fn, tp: 3, 6
    699. 

  699. GB f1 score: 0.706
    700. 

  700. GB cohens kappa score: 0.697
    701. 

  701. 

  702. -> test with 'KNN'
    703. 

  703. KNN tn, fp: 277, 11
    704. 

  704. KNN fn, tp: 1, 8
    705. 

  705. KNN f1 score: 0.571
    706. 

  706. KNN cohens kappa score: 0.553
    707. 

  707. 

  708. 

  709. ------ Step 3/5: Slice 3/5 -------
    710. 

  710. -> Reset the GAN
    711. 

  711. -> Train generator for synthetic samples
    712. 

  712. -> create 1117 synthetic samples
    713. 

  713. -> retrain GAN for predict
    714. 

  714. Epoch 1/10
    715. 

  715. 
  1/116 [..............................] - ETA: 21s - loss: 0.0241
 39/116 [=========>....................] - ETA: 0s - loss: 0.0846 
 76/116 [==================>...........] - ETA: 0s - loss: 0.0844
115/116 [============================>.] - ETA: 0s - loss: 0.0826
116/116 [==============================] - 0s 1ms/step - loss: 0.0825
    716. 

  716. Epoch 2/10
    717. 

  717. 
  1/116 [..............................] - ETA: 0s - loss: 0.0257
 38/116 [========>.....................] - ETA: 0s - loss: 0.0800
 76/116 [==================>...........] - ETA: 0s - loss: 0.0815
113/116 [============================>.] - ETA: 0s - loss: 0.0812
116/116 [==============================] - 0s 1ms/step - loss: 0.0799
    718. 

  718. Epoch 3/10
    719. 

  719. 
  1/116 [..............................] - ETA: 0s - loss: 0.0602
 34/116 [=======>......................] - ETA: 0s - loss: 0.0687
 64/116 [===============>..............] - ETA: 0s - loss: 0.0758
 98/116 [========================>.....] - ETA: 0s - loss: 0.0820
116/116 [==============================] - 0s 2ms/step - loss: 0.0793
    720. 

  720. Epoch 4/10
    721. 

  721. 
  1/116 [..............................] - ETA: 0s - loss: 0.0197
 39/116 [=========>....................] - ETA: 0s - loss: 0.0834
 78/116 [===================>..........] - ETA: 0s - loss: 0.0835
115/116 [============================>.] - ETA: 0s - loss: 0.0780
116/116 [==============================] - 0s 1ms/step - loss: 0.0779
    722. 

  722. Epoch 5/10
    723. 

  723. 
  1/116 [..............................] - ETA: 0s - loss: 0.0153
 37/116 [========>.....................] - ETA: 0s - loss: 0.0655
 74/116 [==================>...........] - ETA: 0s - loss: 0.0683
112/116 [===========================>..] - ETA: 0s - loss: 0.0782
116/116 [==============================] - 0s 1ms/step - loss: 0.0773
    724. 

  724. Epoch 6/10
    725. 

  725. 
  1/116 [..............................] - ETA: 0s - loss: 0.1583
 38/116 [========>.....................] - ETA: 0s - loss: 0.0659
 73/116 [=================>............] - ETA: 0s - loss: 0.0742
110/116 [===========================>..] - ETA: 0s - loss: 0.0718
116/116 [==============================] - 0s 1ms/step - loss: 0.0755
    726. 

  726. Epoch 7/10
    727. 

  727. 
  1/116 [..............................] - ETA: 0s - loss: 0.0164
 39/116 [=========>....................] - ETA: 0s - loss: 0.0730
 76/116 [==================>...........] - ETA: 0s - loss: 0.0732
114/116 [============================>.] - ETA: 0s - loss: 0.0738
116/116 [==============================] - 0s 1ms/step - loss: 0.0740
    728. 

  728. Epoch 8/10
    729. 

  729. 
  1/116 [..............................] - ETA: 0s - loss: 0.0613
 38/116 [========>.....................] - ETA: 0s - loss: 0.0487
 76/116 [==================>...........] - ETA: 0s - loss: 0.0610
112/116 [===========================>..] - ETA: 0s - loss: 0.0742
116/116 [==============================] - 0s 1ms/step - loss: 0.0741
    730. 

  730. Epoch 9/10
    731. 

  731. 
  1/116 [..............................] - ETA: 0s - loss: 0.0306
 38/116 [========>.....................] - ETA: 0s - loss: 0.0757
 76/116 [==================>...........] - ETA: 0s - loss: 0.0692
112/116 [===========================>..] - ETA: 0s - loss: 0.0730
116/116 [==============================] - 0s 1ms/step - loss: 0.0727
    732. 

  732. Epoch 10/10
    733. 

  733. 
  1/116 [..............................] - ETA: 0s - loss: 0.0173
 39/116 [=========>....................] - ETA: 0s - loss: 0.0779
 76/116 [==================>...........] - ETA: 0s - loss: 0.0778
114/116 [============================>.] - ETA: 0s - loss: 0.0714
116/116 [==============================] - 0s 1ms/step - loss: 0.0711
    734. 

  734. -> test with GAN.predict
    735. 

  735. GAN tn, fp: 283, 5
    736. 

  736. GAN fn, tp: 1, 8
    737. 

  737. GAN f1 score: 0.727
    738. 

  738. GAN cohens kappa score: 0.717
    739. 

  739. 

  740. -> test with 'LR'
    741. 

  741. LR tn, fp: 281, 7
    742. 

  742. LR fn, tp: 1, 8
    743. 

  743. LR f1 score: 0.667
    744. 

  744. LR cohens kappa score: 0.654
    745. 

  745. LR average precision score: 0.813
    746. 

  746. 

  747. -> test with 'RF'
    748. 

  748. RF tn, fp: 288, 0
    749. 

  749. RF fn, tp: 5, 4
    750. 

  750. RF f1 score: 0.615
    751. 

  751. RF cohens kappa score: 0.608
    752. 

  752. 

  753. -> test with 'GB'
    754. 

  754. GB tn, fp: 288, 0
    755. 

  755. GB fn, tp: 4, 5
    756. 

  756. GB f1 score: 0.714
    757. 

  757. GB cohens kappa score: 0.708
    758. 

  758. 

  759. -> test with 'KNN'
    760. 

  760. KNN tn, fp: 284, 4
    761. 

  761. KNN fn, tp: 0, 9
    762. 

  762. KNN f1 score: 0.818
    763. 

  763. KNN cohens kappa score: 0.811
    764. 

  764. 

  765. 

  766. ------ Step 3/5: Slice 4/5 -------
    767. 

  767. -> Reset the GAN
    768. 

  768. -> Train generator for synthetic samples
    769. 

  769. -> create 1117 synthetic samples
    770. 

  770. -> retrain GAN for predict
    771. 

  771. Epoch 1/10
    772. 

  772. 
  1/116 [..............................] - ETA: 21s - loss: 0.2269
 35/116 [========>.....................] - ETA: 0s - loss: 0.0845 
 73/116 [=================>............] - ETA: 0s - loss: 0.0841
113/116 [============================>.] - ETA: 0s - loss: 0.0812
116/116 [==============================] - 0s 1ms/step - loss: 0.0821
    773. 

  773. Epoch 2/10
    774. 

  774. 
  1/116 [..............................] - ETA: 0s - loss: 0.0408
 37/116 [========>.....................] - ETA: 0s - loss: 0.0944
 76/116 [==================>...........] - ETA: 0s - loss: 0.0793
114/116 [============================>.] - ETA: 0s - loss: 0.0808
116/116 [==============================] - 0s 1ms/step - loss: 0.0802
    775. 

  775. Epoch 3/10
    776. 

  776. 
  1/116 [..............................] - ETA: 0s - loss: 0.0215
 42/116 [=========>....................] - ETA: 0s - loss: 0.0546
 78/116 [===================>..........] - ETA: 0s - loss: 0.0729
100/116 [========================>.....] - ETA: 0s - loss: 0.0723
116/116 [==============================] - 0s 2ms/step - loss: 0.0779
    777. 

  777. Epoch 4/10
    778. 

  778. 
  1/116 [..............................] - ETA: 0s - loss: 0.0483
 28/116 [======>.......................] - ETA: 0s - loss: 0.0631
 55/116 [=============>................] - ETA: 0s - loss: 0.0737
 76/116 [==================>...........] - ETA: 0s - loss: 0.0826
 97/116 [========================>.....] - ETA: 0s - loss: 0.0786
116/116 [==============================] - 0s 2ms/step - loss: 0.0793
    779. 

  779. Epoch 5/10
    780. 

  780. 
  1/116 [..............................] - ETA: 0s - loss: 0.0244
 29/116 [======>.......................] - ETA: 0s - loss: 0.0819
 54/116 [============>.................] - ETA: 0s - loss: 0.0832
 82/116 [====================>.........] - ETA: 0s - loss: 0.0762
110/116 [===========================>..] - ETA: 0s - loss: 0.0746
116/116 [==============================] - 0s 2ms/step - loss: 0.0763
    781. 

  781. Epoch 6/10
    782. 

  782. 
  1/116 [..............................] - ETA: 0s - loss: 0.0314
 28/116 [======>.......................] - ETA: 0s - loss: 0.0676
 57/116 [=============>................] - ETA: 0s - loss: 0.0762
 84/116 [====================>.........] - ETA: 0s - loss: 0.0758
108/116 [==========================>...] - ETA: 0s - loss: 0.0718
116/116 [==============================] - 0s 2ms/step - loss: 0.0749
    783. 

  783. Epoch 7/10
    784. 

  784. 
  1/116 [..............................] - ETA: 0s - loss: 0.0659
 30/116 [======>.......................] - ETA: 0s - loss: 0.0846
 59/116 [==============>...............] - ETA: 0s - loss: 0.0675
 86/116 [=====================>........] - ETA: 0s - loss: 0.0664
113/116 [============================>.] - ETA: 0s - loss: 0.0717
116/116 [==============================] - 0s 2ms/step - loss: 0.0726
    785. 

  785. Epoch 8/10
    786. 

  786. 
  1/116 [..............................] - ETA: 0s - loss: 0.0883
 31/116 [=======>......................] - ETA: 0s - loss: 0.0631
 63/116 [===============>..............] - ETA: 0s - loss: 0.0578
 99/116 [========================>.....] - ETA: 0s - loss: 0.0756
116/116 [==============================] - 0s 2ms/step - loss: 0.0718
    787. 

  787. Epoch 9/10
    788. 

  788. 
  1/116 [..............................] - ETA: 0s - loss: 0.0083
 24/116 [=====>........................] - ETA: 0s - loss: 0.0624
 49/116 [===========>..................] - ETA: 0s - loss: 0.0523
 74/116 [==================>...........] - ETA: 0s - loss: 0.0548
 94/116 [=======================>......] - ETA: 0s - loss: 0.0697
116/116 [==============================] - 0s 2ms/step - loss: 0.0710
    789. 

  789. Epoch 10/10
    790. 

  790. 
  1/116 [..............................] - ETA: 0s - loss: 0.0250
 26/116 [=====>........................] - ETA: 0s - loss: 0.0883
 55/116 [=============>................] - ETA: 0s - loss: 0.0679
 84/116 [====================>.........] - ETA: 0s - loss: 0.0758
114/116 [============================>.] - ETA: 0s - loss: 0.0699
116/116 [==============================] - 0s 2ms/step - loss: 0.0696
    791. 

  791. -> test with GAN.predict
    792. 

  792. GAN tn, fp: 281, 7
    793. 

  793. GAN fn, tp: 0, 9
    794. 

  794. GAN f1 score: 0.720
    795. 

  795. GAN cohens kappa score: 0.709
    796. 

  796. 

  797. -> test with 'LR'
    798. 

  798. LR tn, fp: 278, 10
    799. 

  799. LR fn, tp: 0, 9
    800. 

  800. LR f1 score: 0.643
    801. 

  801. LR cohens kappa score: 0.628
    802. 

  802. LR average precision score: 0.765
    803. 

  803. 

  804. -> test with 'RF'
    805. 

  805. RF tn, fp: 288, 0
    806. 

  806. RF fn, tp: 5, 4
    807. 

  807. RF f1 score: 0.615
    808. 

  808. RF cohens kappa score: 0.608
    809. 

  809. 

  810. -> test with 'GB'
    811. 

  811. GB tn, fp: 288, 0
    812. 

  812. GB fn, tp: 4, 5
    813. 

  813. GB f1 score: 0.714
    814. 

  814. GB cohens kappa score: 0.708
    815. 

  815. 

  816. -> test with 'KNN'
    817. 

  817. KNN tn, fp: 280, 8
    818. 

  818. KNN fn, tp: 1, 8
    819. 

  819. KNN f1 score: 0.640
    820. 

  820. KNN cohens kappa score: 0.625
    821. 

  821. 

  822. 

  823. ------ Step 3/5: Slice 5/5 -------
    824. 

  824. -> Reset the GAN
    825. 

  825. -> Train generator for synthetic samples
    826. 

  826. -> create 1116 synthetic samples
    827. 

  827. -> retrain GAN for predict
    828. 

  828. Epoch 1/10
    829. 

  829. 
  1/116 [..............................] - ETA: 23s - loss: 0.1531
 41/116 [=========>....................] - ETA: 0s - loss: 0.0677 
 79/116 [===================>..........] - ETA: 0s - loss: 0.0722
115/116 [============================>.] - ETA: 0s - loss: 0.0757
116/116 [==============================] - 0s 1ms/step - loss: 0.0756
    830. 

  830. Epoch 2/10
    831. 

  831. 
  1/116 [..............................] - ETA: 0s - loss: 0.0659
 38/116 [========>.....................] - ETA: 0s - loss: 0.0900
 76/116 [==================>...........] - ETA: 0s - loss: 0.0799
115/116 [============================>.] - ETA: 0s - loss: 0.0742
116/116 [==============================] - 0s 1ms/step - loss: 0.0741
    832. 

  832. Epoch 3/10
    833. 

  833. 
  1/116 [..............................] - ETA: 0s - loss: 0.0743
 40/116 [=========>....................] - ETA: 0s - loss: 0.0760
 80/116 [===================>..........] - ETA: 0s - loss: 0.0825
116/116 [==============================] - 0s 1ms/step - loss: 0.0736
    834. 

  834. Epoch 4/10
    835. 

  835. 
  1/116 [..............................] - ETA: 0s - loss: 0.0203
 40/116 [=========>....................] - ETA: 0s - loss: 0.0737
 77/116 [==================>...........] - ETA: 0s - loss: 0.0724
114/116 [============================>.] - ETA: 0s - loss: 0.0726
116/116 [==============================] - 0s 1ms/step - loss: 0.0726
    836. 

  836. Epoch 5/10
    837. 

  837. 
  1/116 [..............................] - ETA: 0s - loss: 0.0308
 39/116 [=========>....................] - ETA: 0s - loss: 0.0742
 77/116 [==================>...........] - ETA: 0s - loss: 0.0727
111/116 [===========================>..] - ETA: 0s - loss: 0.0701
116/116 [==============================] - 0s 1ms/step - loss: 0.0705
    838. 

  838. Epoch 6/10
    839. 

  839. 
  1/116 [..............................] - ETA: 0s - loss: 0.0163
 36/116 [========>.....................] - ETA: 0s - loss: 0.0593
 74/116 [==================>...........] - ETA: 0s - loss: 0.0592
113/116 [============================>.] - ETA: 0s - loss: 0.0699
116/116 [==============================] - 0s 1ms/step - loss: 0.0689
    840. 

  840. Epoch 7/10
    841. 

  841. 
  1/116 [..............................] - ETA: 0s - loss: 0.0506
 40/116 [=========>....................] - ETA: 0s - loss: 0.0593
 78/116 [===================>..........] - ETA: 0s - loss: 0.0630
116/116 [==============================] - 0s 1ms/step - loss: 0.0699
    842. 

  842. Epoch 8/10
    843. 

  843. 
  1/116 [..............................] - ETA: 0s - loss: 0.3316
 40/116 [=========>....................] - ETA: 0s - loss: 0.0789
 79/116 [===================>..........] - ETA: 0s - loss: 0.0670
114/116 [============================>.] - ETA: 0s - loss: 0.0675
116/116 [==============================] - 0s 1ms/step - loss: 0.0688
    844. 

  844. Epoch 9/10
    845. 

  845. 
  1/116 [..............................] - ETA: 0s - loss: 0.3351
 38/116 [========>.....................] - ETA: 0s - loss: 0.0679
 72/116 [=================>............] - ETA: 0s - loss: 0.0686
111/116 [===========================>..] - ETA: 0s - loss: 0.0686
116/116 [==============================] - 0s 1ms/step - loss: 0.0682
    846. 

  846. Epoch 10/10
    847. 

  847. 
  1/116 [..............................] - ETA: 0s - loss: 0.0100
 39/116 [=========>....................] - ETA: 0s - loss: 0.0672
 76/116 [==================>...........] - ETA: 0s - loss: 0.0698
113/116 [============================>.] - ETA: 0s - loss: 0.0677
116/116 [==============================] - 0s 1ms/step - loss: 0.0672
    848. 

  848. -> test with GAN.predict
    849. 

  849. GAN tn, fp: 274, 14
    850. 

  850. GAN fn, tp: 1, 7
    851. 

  851. GAN f1 score: 0.483
    852. 

  852. GAN cohens kappa score: 0.462
    853. 

  853. 

  854. -> test with 'LR'
    855. 

  855. LR tn, fp: 275, 13
    856. 

  856. LR fn, tp: 0, 8
    857. 

  857. LR f1 score: 0.552
    858. 

  858. LR cohens kappa score: 0.533
    859. 

  859. LR average precision score: 0.304
    860. 

  860. 

  861. -> test with 'RF'
    862. 

  862. RF tn, fp: 284, 4
    863. 

  863. RF fn, tp: 2, 6
    864. 

  864. RF f1 score: 0.667
    865. 

  865. RF cohens kappa score: 0.656
    866. 

  866. 

  867. -> test with 'GB'
    868. 

  868. GB tn, fp: 282, 6
    869. 

  869. GB fn, tp: 1, 7
    870. 

  870. GB f1 score: 0.667
    871. 

  871. GB cohens kappa score: 0.655
    872. 

  872. 

  873. -> test with 'KNN'
    874. 

  874. KNN tn, fp: 276, 12
    875. 

  875. KNN fn, tp: 0, 8
    876. 

  876. KNN f1 score: 0.571
    877. 

  877. KNN cohens kappa score: 0.554
    878. 

  878. 

  879. 

  880. ====== Step 4/5 =======
    881. 

  881. -> Shuffling data
    882. 

  882. -> Spliting data to slices
    883. 

  883. 

  884. ------ Step 4/5: Slice 1/5 -------
    885. 

  885. -> Reset the GAN
    886. 

  886. -> Train generator for synthetic samples
    887. 

  887. -> create 1117 synthetic samples
    888. 

  888. -> retrain GAN for predict
    889. 

  889. Epoch 1/10
    890. 

  890. 
  1/116 [..............................] - ETA: 26s - loss: 0.0502
 29/116 [======>.......................] - ETA: 0s - loss: 0.0929 
 56/116 [=============>................] - ETA: 0s - loss: 0.0911
 81/116 [===================>..........] - ETA: 0s - loss: 0.0831
 98/116 [========================>.....] - ETA: 0s - loss: 0.0843
116/116 [==============================] - 0s 2ms/step - loss: 0.0830
    891. 

  891. Epoch 2/10
    892. 

  892. 
  1/116 [..............................] - ETA: 0s - loss: 0.0258
 22/116 [====>.........................] - ETA: 0s - loss: 0.0724
 38/116 [========>.....................] - ETA: 0s - loss: 0.0786
 54/116 [============>.................] - ETA: 0s - loss: 0.0803
 71/116 [=================>............] - ETA: 0s - loss: 0.0811
 91/116 [======================>.......] - ETA: 0s - loss: 0.0775
113/116 [============================>.] - ETA: 0s - loss: 0.0814
116/116 [==============================] - 0s 3ms/step - loss: 0.0821
    893. 

  893. Epoch 3/10
    894. 

  894. 
  1/116 [..............................] - ETA: 0s - loss: 0.1194
 23/116 [====>.........................] - ETA: 0s - loss: 0.0640
 44/116 [==========>...................] - ETA: 0s - loss: 0.0676
 60/116 [==============>...............] - ETA: 0s - loss: 0.0736
 75/116 [==================>...........] - ETA: 0s - loss: 0.0746
 88/116 [=====================>........] - ETA: 0s - loss: 0.0764
106/116 [==========================>...] - ETA: 0s - loss: 0.0777
116/116 [==============================] - 0s 3ms/step - loss: 0.0807
    895. 

  895. Epoch 4/10
    896. 

  896. 
  1/116 [..............................] - ETA: 0s - loss: 0.0360
 18/116 [===>..........................] - ETA: 0s - loss: 0.0655
 34/116 [=======>......................] - ETA: 0s - loss: 0.0902
 50/116 [===========>..................] - ETA: 0s - loss: 0.0918
 67/116 [================>.............] - ETA: 0s - loss: 0.0883
 82/116 [====================>.........] - ETA: 0s - loss: 0.0910
 99/116 [========================>.....] - ETA: 0s - loss: 0.0815
116/116 [==============================] - 0s 3ms/step - loss: 0.0792
    897. 

  897. Epoch 5/10
    898. 

  898. 
  1/116 [..............................] - ETA: 0s - loss: 0.0084
 20/116 [====>.........................] - ETA: 0s - loss: 0.0959
 36/116 [========>.....................] - ETA: 0s - loss: 0.0842
 59/116 [==============>...............] - ETA: 0s - loss: 0.0887
 80/116 [===================>..........] - ETA: 0s - loss: 0.0875
101/116 [=========================>....] - ETA: 0s - loss: 0.0786
116/116 [==============================] - 0s 3ms/step - loss: 0.0785
    899. 

  899. Epoch 6/10
    900. 

  900. 
  1/116 [..............................] - ETA: 0s - loss: 0.0257
 18/116 [===>..........................] - ETA: 0s - loss: 0.0713
 33/116 [=======>......................] - ETA: 0s - loss: 0.0883
 52/116 [============>.................] - ETA: 0s - loss: 0.0859
 72/116 [=================>............] - ETA: 0s - loss: 0.0822
 94/116 [=======================>......] - ETA: 0s - loss: 0.0842
116/116 [==============================] - 0s 3ms/step - loss: 0.0777
    901. 

  901. Epoch 7/10
    902. 

  902. 
  1/116 [..............................] - ETA: 0s - loss: 0.1209
 26/116 [=====>........................] - ETA: 0s - loss: 0.0678
 48/116 [===========>..................] - ETA: 0s - loss: 0.0758
 69/116 [================>.............] - ETA: 0s - loss: 0.0697
 89/116 [======================>.......] - ETA: 0s - loss: 0.0692
110/116 [===========================>..] - ETA: 0s - loss: 0.0784
116/116 [==============================] - 0s 2ms/step - loss: 0.0779
    903. 

  903. Epoch 8/10
    904. 

  904. 
  1/116 [..............................] - ETA: 0s - loss: 0.0564
 25/116 [=====>........................] - ETA: 0s - loss: 0.0814
 48/116 [===========>..................] - ETA: 0s - loss: 0.1026
 71/116 [=================>............] - ETA: 0s - loss: 0.0898
 92/116 [======================>.......] - ETA: 0s - loss: 0.0821
116/116 [==============================] - 0s 2ms/step - loss: 0.0765
    905. 

  905. Epoch 9/10
    906. 

  906. 
  1/116 [..............................] - ETA: 0s - loss: 0.2265
 29/116 [======>.......................] - ETA: 0s - loss: 0.0733
 52/116 [============>.................] - ETA: 0s - loss: 0.0806
 82/116 [====================>.........] - ETA: 0s - loss: 0.0750
112/116 [===========================>..] - ETA: 0s - loss: 0.0760
116/116 [==============================] - 0s 2ms/step - loss: 0.0762
    907. 

  907. Epoch 10/10
    908. 

  908. 
  1/116 [..............................] - ETA: 0s - loss: 0.0092
 25/116 [=====>........................] - ETA: 0s - loss: 0.0537
 49/116 [===========>..................] - ETA: 0s - loss: 0.0817
 76/116 [==================>...........] - ETA: 0s - loss: 0.0772
106/116 [==========================>...] - ETA: 0s - loss: 0.0770
116/116 [==============================] - 0s 2ms/step - loss: 0.0759
    909. 

  909. -> test with GAN.predict
    910. 

  910. GAN tn, fp: 276, 12
    911. 

  911. GAN fn, tp: 0, 9
    912. 

  912. GAN f1 score: 0.600
    913. 

  913. GAN cohens kappa score: 0.582
    914. 

  914. 

  915. -> test with 'LR'
    916. 

  916. LR tn, fp: 275, 13
    917. 

  917. LR fn, tp: 0, 9
    918. 

  918. LR f1 score: 0.581
    919. 

  919. LR cohens kappa score: 0.562
    920. 

  920. LR average precision score: 0.714
    921. 

  921. 

  922. -> test with 'RF'
    923. 

  923. RF tn, fp: 286, 2
    924. 

  924. RF fn, tp: 3, 6
    925. 

  925. RF f1 score: 0.706
    926. 

  926. RF cohens kappa score: 0.697
    927. 

  927. 

  928. -> test with 'GB'
    929. 

  929. GB tn, fp: 286, 2
    930. 

  930. GB fn, tp: 1, 8
    931. 

  931. GB f1 score: 0.842
    932. 

  932. GB cohens kappa score: 0.837
    933. 

  933. 

  934. -> test with 'KNN'
    935. 

  935. KNN tn, fp: 276, 12
    936. 

  936. KNN fn, tp: 0, 9
    937. 

  937. KNN f1 score: 0.600
    938. 

  938. KNN cohens kappa score: 0.582
    939. 

  939. 

  940. 

  941. ------ Step 4/5: Slice 2/5 -------
    942. 

  942. -> Reset the GAN
    943. 

  943. -> Train generator for synthetic samples
    944. 

  944. -> create 1117 synthetic samples
    945. 

  945. -> retrain GAN for predict
    946. 

  946. Epoch 1/10
    947. 

  947. 
  1/116 [..............................] - ETA: 20s - loss: 0.1457
 39/116 [=========>....................] - ETA: 0s - loss: 0.0739 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0724
116/116 [==============================] - 0s 1ms/step - loss: 0.0709
    948. 

  948. Epoch 2/10
    949. 

  949. 
  1/116 [..............................] - ETA: 0s - loss: 0.0197
 39/116 [=========>....................] - ETA: 0s - loss: 0.0611
 78/116 [===================>..........] - ETA: 0s - loss: 0.0656
116/116 [==============================] - ETA: 0s - loss: 0.0682
116/116 [==============================] - 0s 1ms/step - loss: 0.0682
    950. 

  950. Epoch 3/10
    951. 

  951. 
  1/116 [..............................] - ETA: 0s - loss: 0.0368
 42/116 [=========>....................] - ETA: 0s - loss: 0.0742
 82/116 [====================>.........] - ETA: 0s - loss: 0.0618
116/116 [==============================] - 0s 1ms/step - loss: 0.0691
    952. 

  952. Epoch 4/10
    953. 

  953. 
  1/116 [..............................] - ETA: 0s - loss: 0.0225
 34/116 [=======>......................] - ETA: 0s - loss: 0.0521
 66/116 [================>.............] - ETA: 0s - loss: 0.0567
101/116 [=========================>....] - ETA: 0s - loss: 0.0604
116/116 [==============================] - 0s 1ms/step - loss: 0.0682
    954. 

  954. Epoch 5/10
    955. 

  955. 
  1/116 [..............................] - ETA: 0s - loss: 0.0331
 39/116 [=========>....................] - ETA: 0s - loss: 0.0678
 78/116 [===================>..........] - ETA: 0s - loss: 0.0594
116/116 [==============================] - 0s 1ms/step - loss: 0.0673
    956. 

  956. Epoch 6/10
    957. 

  957. 
  1/116 [..............................] - ETA: 0s - loss: 0.0637
 33/116 [=======>......................] - ETA: 0s - loss: 0.0541
 72/116 [=================>............] - ETA: 0s - loss: 0.0647
108/116 [==========================>...] - ETA: 0s - loss: 0.0636
116/116 [==============================] - 0s 1ms/step - loss: 0.0665
    958. 

  958. Epoch 7/10
    959. 

  959. 
  1/116 [..............................] - ETA: 0s - loss: 0.0134
 42/116 [=========>....................] - ETA: 0s - loss: 0.0595
 78/116 [===================>..........] - ETA: 0s - loss: 0.0642
116/116 [==============================] - 0s 1ms/step - loss: 0.0660
    960. 

  960. Epoch 8/10
    961. 

  961. 
  1/116 [..............................] - ETA: 0s - loss: 0.0221
 41/116 [=========>....................] - ETA: 0s - loss: 0.0623
 79/116 [===================>..........] - ETA: 0s - loss: 0.0607
116/116 [==============================] - ETA: 0s - loss: 0.0653
116/116 [==============================] - 0s 1ms/step - loss: 0.0653
    962. 

  962. Epoch 9/10
    963. 

  963. 
  1/116 [..............................] - ETA: 0s - loss: 0.0306
 37/116 [========>.....................] - ETA: 0s - loss: 0.0671
 75/116 [==================>...........] - ETA: 0s - loss: 0.0633
108/116 [==========================>...] - ETA: 0s - loss: 0.0604
116/116 [==============================] - 0s 1ms/step - loss: 0.0651
    964. 

  964. Epoch 10/10
    965. 

  965. 
  1/116 [..............................] - ETA: 0s - loss: 0.0264
 34/116 [=======>......................] - ETA: 0s - loss: 0.0847
 71/116 [=================>............] - ETA: 0s - loss: 0.0682
107/116 [==========================>...] - ETA: 0s - loss: 0.0611
116/116 [==============================] - 0s 1ms/step - loss: 0.0631
    966. 

  966. -> test with GAN.predict
    967. 

  967. GAN tn, fp: 274, 14
    968. 

  968. GAN fn, tp: 0, 9
    969. 

  969. GAN f1 score: 0.562
    970. 

  970. GAN cohens kappa score: 0.543
    971. 

  971. 

  972. -> test with 'LR'
    973. 

  973. LR tn, fp: 271, 17
    974. 

  974. LR fn, tp: 0, 9
    975. 

  975. LR f1 score: 0.514
    976. 

  976. LR cohens kappa score: 0.491
    977. 

  977. LR average precision score: 0.623
    978. 

  978. 

  979. -> test with 'RF'
    980. 

  980. RF tn, fp: 287, 1
    981. 

  981. RF fn, tp: 2, 7
    982. 

  982. RF f1 score: 0.824
    983. 

  983. RF cohens kappa score: 0.818
    984. 

  984. 

  985. -> test with 'GB'
    986. 

  986. GB tn, fp: 287, 1
    987. 

  987. GB fn, tp: 2, 7
    988. 

  988. GB f1 score: 0.824
    989. 

  989. GB cohens kappa score: 0.818
    990. 

  990. 

  991. -> test with 'KNN'
    992. 

  992. KNN tn, fp: 277, 11
    993. 

  993. KNN fn, tp: 0, 9
    994. 

  994. KNN f1 score: 0.621
    995. 

  995. KNN cohens kappa score: 0.604
    996. 

  996. 

  997. 

  998. ------ Step 4/5: Slice 3/5 -------
    999. 

  999. -> Reset the GAN
    1000. 

  1000. -> Train generator for synthetic samples
    1001. 

  1001. -> create 1117 synthetic samples
    1002. 

  1002. -> retrain GAN for predict
    1003. 

  1003. Epoch 1/10
    1004. 

  1004. 
  1/116 [..............................] - ETA: 18s - loss: 0.0899
 37/116 [========>.....................] - ETA: 0s - loss: 0.1073 
 69/116 [================>.............] - ETA: 0s - loss: 0.0863
108/116 [==========================>...] - ETA: 0s - loss: 0.0846
116/116 [==============================] - 0s 1ms/step - loss: 0.0830
    1005. 

  1005. Epoch 2/10
    1006. 

  1006. 
  1/116 [..............................] - ETA: 0s - loss: 0.2157
 35/116 [========>.....................] - ETA: 0s - loss: 0.0791
 67/116 [================>.............] - ETA: 0s - loss: 0.0747
102/116 [=========================>....] - ETA: 0s - loss: 0.0792
116/116 [==============================] - 0s 1ms/step - loss: 0.0789
    1007. 

  1007. Epoch 3/10
    1008. 

  1008. 
  1/116 [..............................] - ETA: 0s - loss: 0.1671
 35/116 [========>.....................] - ETA: 0s - loss: 0.0833
 68/116 [================>.............] - ETA: 0s - loss: 0.0775
 89/116 [======================>.......] - ETA: 0s - loss: 0.0754
116/116 [==============================] - 0s 2ms/step - loss: 0.0785
    1009. 

  1009. Epoch 4/10
    1010. 

  1010. 
  1/116 [..............................] - ETA: 0s - loss: 0.3553
 32/116 [=======>......................] - ETA: 0s - loss: 0.0581
 66/116 [================>.............] - ETA: 0s - loss: 0.0702
101/116 [=========================>....] - ETA: 0s - loss: 0.0732
116/116 [==============================] - 0s 2ms/step - loss: 0.0762
    1011. 

  1011. Epoch 5/10
    1012. 

  1012. 
  1/116 [..............................] - ETA: 0s - loss: 0.0870
 37/116 [========>.....................] - ETA: 0s - loss: 0.0657
 68/116 [================>.............] - ETA: 0s - loss: 0.0778
100/116 [========================>.....] - ETA: 0s - loss: 0.0786
116/116 [==============================] - 0s 2ms/step - loss: 0.0749
    1013. 

  1013. Epoch 6/10
    1014. 

  1014. 
  1/116 [..............................] - ETA: 0s - loss: 0.0071
 32/116 [=======>......................] - ETA: 0s - loss: 0.0598
 69/116 [================>.............] - ETA: 0s - loss: 0.0689
102/116 [=========================>....] - ETA: 0s - loss: 0.0761
116/116 [==============================] - 0s 2ms/step - loss: 0.0760
    1015. 

  1015. Epoch 7/10
    1016. 

  1016. 
  1/116 [..............................] - ETA: 0s - loss: 0.0379
 35/116 [========>.....................] - ETA: 0s - loss: 0.0761
 68/116 [================>.............] - ETA: 0s - loss: 0.0800
102/116 [=========================>....] - ETA: 0s - loss: 0.0746
116/116 [==============================] - 0s 2ms/step - loss: 0.0740
    1017. 

  1017. Epoch 8/10
    1018. 

  1018. 
  1/116 [..............................] - ETA: 0s - loss: 0.0384
 36/116 [========>.....................] - ETA: 0s - loss: 0.0599
 73/116 [=================>............] - ETA: 0s - loss: 0.0662
111/116 [===========================>..] - ETA: 0s - loss: 0.0729
116/116 [==============================] - 0s 1ms/step - loss: 0.0734
    1019. 

  1019. Epoch 9/10
    1020. 

  1020. 
  1/116 [..............................] - ETA: 0s - loss: 0.0212
 34/116 [=======>......................] - ETA: 0s - loss: 0.0562
 61/116 [==============>...............] - ETA: 0s - loss: 0.0693
 99/116 [========================>.....] - ETA: 0s - loss: 0.0713
116/116 [==============================] - 0s 2ms/step - loss: 0.0727
    1021. 

  1021. Epoch 10/10
    1022. 

  1022. 
  1/116 [..............................] - ETA: 0s - loss: 0.0672
 33/116 [=======>......................] - ETA: 0s - loss: 0.0808
 68/116 [================>.............] - ETA: 0s - loss: 0.0741
104/116 [=========================>....] - ETA: 0s - loss: 0.0725
116/116 [==============================] - 0s 1ms/step - loss: 0.0714
    1023. 

  1023. -> test with GAN.predict
    1024. 

  1024. GAN tn, fp: 279, 9
    1025. 

  1025. GAN fn, tp: 2, 7
    1026. 

  1026. GAN f1 score: 0.560
    1027. 

  1027. GAN cohens kappa score: 0.542
    1028. 

  1028. 

  1029. -> test with 'LR'
    1030. 

  1030. LR tn, fp: 278, 10
    1031. 

  1031. LR fn, tp: 2, 7
    1032. 

  1032. LR f1 score: 0.538
    1033. 

  1033. LR cohens kappa score: 0.519
    1034. 

  1034. LR average precision score: 0.615
    1035. 

  1035. 

  1036. -> test with 'RF'
    1037. 

  1037. RF tn, fp: 283, 5
    1038. 

  1038. RF fn, tp: 4, 5
    1039. 

  1039. RF f1 score: 0.526
    1040. 

  1040. RF cohens kappa score: 0.511
    1041. 

  1041. 

  1042. -> test with 'GB'
    1043. 

  1043. GB tn, fp: 283, 5
    1044. 

  1044. GB fn, tp: 4, 5
    1045. 

  1045. GB f1 score: 0.526
    1046. 

  1046. GB cohens kappa score: 0.511
    1047. 

  1047. 

  1048. -> test with 'KNN'
    1049. 

  1049. KNN tn, fp: 279, 9
    1050. 

  1050. KNN fn, tp: 0, 9
    1051. 

  1051. KNN f1 score: 0.667
    1052. 

  1052. KNN cohens kappa score: 0.653
    1053. 

  1053. 

  1054. 

  1055. ------ Step 4/5: Slice 4/5 -------
    1056. 

  1056. -> Reset the GAN
    1057. 

  1057. -> Train generator for synthetic samples
    1058. 

  1058. -> create 1117 synthetic samples
    1059. 

  1059. -> retrain GAN for predict
    1060. 

  1060. Epoch 1/10
    1061. 

  1061. 
  1/116 [..............................] - ETA: 21s - loss: 0.0619
 39/116 [=========>....................] - ETA: 0s - loss: 0.0976 
 80/116 [===================>..........] - ETA: 0s - loss: 0.0815
116/116 [==============================] - 0s 1ms/step - loss: 0.0833
    1062. 

  1062. Epoch 2/10
    1063. 

  1063. 
  1/116 [..............................] - ETA: 0s - loss: 0.0235
 42/116 [=========>....................] - ETA: 0s - loss: 0.0577
 80/116 [===================>..........] - ETA: 0s - loss: 0.0748
114/116 [============================>.] - ETA: 0s - loss: 0.0813
116/116 [==============================] - 0s 1ms/step - loss: 0.0809
    1064. 

  1064. Epoch 3/10
    1065. 

  1065. 
  1/116 [..............................] - ETA: 0s - loss: 0.2452
 34/116 [=======>......................] - ETA: 0s - loss: 0.0679
 68/116 [================>.............] - ETA: 0s - loss: 0.0760
108/116 [==========================>...] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0798
    1066. 

  1066. Epoch 4/10
    1067. 

  1067. 
  1/116 [..............................] - ETA: 0s - loss: 0.0294
 42/116 [=========>....................] - ETA: 0s - loss: 0.0847
 83/116 [====================>.........] - ETA: 0s - loss: 0.0752
116/116 [==============================] - 0s 1ms/step - loss: 0.0797
    1068. 

  1068. Epoch 5/10
    1069. 

  1069. 
  1/116 [..............................] - ETA: 0s - loss: 0.2461
 39/116 [=========>....................] - ETA: 0s - loss: 0.0832
 78/116 [===================>..........] - ETA: 0s - loss: 0.0794
113/116 [============================>.] - ETA: 0s - loss: 0.0771
116/116 [==============================] - 0s 1ms/step - loss: 0.0779
    1070. 

  1070. Epoch 6/10
    1071. 

  1071. 
  1/116 [..............................] - ETA: 0s - loss: 0.2621
 39/116 [=========>....................] - ETA: 0s - loss: 0.0665
 73/116 [=================>............] - ETA: 0s - loss: 0.0680
105/116 [==========================>...] - ETA: 0s - loss: 0.0760
116/116 [==============================] - 0s 1ms/step - loss: 0.0773
    1072. 

  1072. Epoch 7/10
    1073. 

  1073. 
  1/116 [..............................] - ETA: 0s - loss: 0.0786
 41/116 [=========>....................] - ETA: 0s - loss: 0.0861
 80/116 [===================>..........] - ETA: 0s - loss: 0.0753
116/116 [==============================] - 0s 1ms/step - loss: 0.0761
    1074. 

  1074. Epoch 8/10
    1075. 

  1075. 
  1/116 [..............................] - ETA: 0s - loss: 0.0084
 41/116 [=========>....................] - ETA: 0s - loss: 0.0827
 81/116 [===================>..........] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0760
    1076. 

  1076. Epoch 9/10
    1077. 

  1077. 
  1/116 [..............................] - ETA: 0s - loss: 0.3655
 42/116 [=========>....................] - ETA: 0s - loss: 0.0788
 83/116 [====================>.........] - ETA: 0s - loss: 0.0795
116/116 [==============================] - 0s 1ms/step - loss: 0.0747
    1078. 

  1078. Epoch 10/10
    1079. 

  1079. 
  1/116 [..............................] - ETA: 0s - loss: 0.0177
 40/116 [=========>....................] - ETA: 0s - loss: 0.0771
 81/116 [===================>..........] - ETA: 0s - loss: 0.0713
116/116 [==============================] - 0s 1ms/step - loss: 0.0738
    1080. 

  1080. -> test with GAN.predict
    1081. 

  1081. GAN tn, fp: 280, 8
    1082. 

  1082. GAN fn, tp: 0, 9
    1083. 

  1083. GAN f1 score: 0.692
    1084. 

  1084. GAN cohens kappa score: 0.680
    1085. 

  1085. 

  1086. -> test with 'LR'
    1087. 

  1087. LR tn, fp: 280, 8
    1088. 

  1088. LR fn, tp: 0, 9
    1089. 

  1089. LR f1 score: 0.692
    1090. 

  1090. LR cohens kappa score: 0.680
    1091. 

  1091. LR average precision score: 0.668
    1092. 

  1092. 

  1093. -> test with 'RF'
    1094. 

  1094. RF tn, fp: 288, 0
    1095. 

  1095. RF fn, tp: 5, 4
    1096. 

  1096. RF f1 score: 0.615
    1097. 

  1097. RF cohens kappa score: 0.608
    1098. 

  1098. 

  1099. -> test with 'GB'
    1100. 

  1100. GB tn, fp: 287, 1
    1101. 

  1101. GB fn, tp: 3, 6
    1102. 

  1102. GB f1 score: 0.750
    1103. 

  1103. GB cohens kappa score: 0.743
    1104. 

  1104. 

  1105. -> test with 'KNN'
    1106. 

  1106. KNN tn, fp: 281, 7
    1107. 

  1107. KNN fn, tp: 1, 8
    1108. 

  1108. KNN f1 score: 0.667
    1109. 

  1109. KNN cohens kappa score: 0.654
    1110. 

  1110. 

  1111. 

  1112. ------ Step 4/5: Slice 5/5 -------
    1113. 

  1113. -> Reset the GAN
    1114. 

  1114. -> Train generator for synthetic samples
    1115. 

  1115. -> create 1116 synthetic samples
    1116. 

  1116. -> retrain GAN for predict
    1117. 

  1117. Epoch 1/10
    1118. 

  1118. 
  1/116 [..............................] - ETA: 18s - loss: 0.0148
 40/116 [=========>....................] - ETA: 0s - loss: 0.0825 
 78/116 [===================>..........] - ETA: 0s - loss: 0.0773
115/116 [============================>.] - ETA: 0s - loss: 0.0797
116/116 [==============================] - 0s 1ms/step - loss: 0.0796
    1119. 

  1119. Epoch 2/10
    1120. 

  1120. 
  1/116 [..............................] - ETA: 0s - loss: 0.0490
 38/116 [========>.....................] - ETA: 0s - loss: 0.0704
 76/116 [==================>...........] - ETA: 0s - loss: 0.0814
116/116 [==============================] - 0s 1ms/step - loss: 0.0791
    1121. 

  1121. Epoch 3/10
    1122. 

  1122. 
  1/116 [..............................] - ETA: 0s - loss: 0.0256
 40/116 [=========>....................] - ETA: 0s - loss: 0.0767
 79/116 [===================>..........] - ETA: 0s - loss: 0.0804
116/116 [==============================] - 0s 1ms/step - loss: 0.0779
    1123. 

  1123. Epoch 4/10
    1124. 

  1124. 
  1/116 [..............................] - ETA: 0s - loss: 0.2742
 39/116 [=========>....................] - ETA: 0s - loss: 0.0927
 79/116 [===================>..........] - ETA: 0s - loss: 0.0798
116/116 [==============================] - ETA: 0s - loss: 0.0777
116/116 [==============================] - 0s 1ms/step - loss: 0.0777
    1125. 

  1125. Epoch 5/10
    1126. 

  1126. 
  1/116 [..............................] - ETA: 0s - loss: 0.1105
 25/116 [=====>........................] - ETA: 0s - loss: 0.0729
 61/116 [==============>...............] - ETA: 0s - loss: 0.0801
100/116 [========================>.....] - ETA: 0s - loss: 0.0781
116/116 [==============================] - 0s 2ms/step - loss: 0.0775
    1127. 

  1127. Epoch 6/10
    1128. 

  1128. 
  1/116 [..............................] - ETA: 0s - loss: 0.0088
 39/116 [=========>....................] - ETA: 0s - loss: 0.0725
 78/116 [===================>..........] - ETA: 0s - loss: 0.0739
116/116 [==============================] - ETA: 0s - loss: 0.0749
116/116 [==============================] - 0s 1ms/step - loss: 0.0749
    1129. 

  1129. Epoch 7/10
    1130. 

  1130. 
  1/116 [..............................] - ETA: 0s - loss: 0.2657
 41/116 [=========>....................] - ETA: 0s - loss: 0.0718
 79/116 [===================>..........] - ETA: 0s - loss: 0.0664
116/116 [==============================] - 0s 1ms/step - loss: 0.0749
    1131. 

  1131. Epoch 8/10
    1132. 

  1132. 
  1/116 [..............................] - ETA: 0s - loss: 0.0320
 41/116 [=========>....................] - ETA: 0s - loss: 0.0679
 78/116 [===================>..........] - ETA: 0s - loss: 0.0783
116/116 [==============================] - 0s 1ms/step - loss: 0.0740
    1133. 

  1133. Epoch 9/10
    1134. 

  1134. 
  1/116 [..............................] - ETA: 0s - loss: 0.1506
 41/116 [=========>....................] - ETA: 0s - loss: 0.1004
 80/116 [===================>..........] - ETA: 0s - loss: 0.0862
116/116 [==============================] - 0s 1ms/step - loss: 0.0740
    1135. 

  1135. Epoch 10/10
    1136. 

  1136. 
  1/116 [..............................] - ETA: 0s - loss: 0.0155
 39/116 [=========>....................] - ETA: 0s - loss: 0.0591
 79/116 [===================>..........] - ETA: 0s - loss: 0.0682
116/116 [==============================] - 0s 1ms/step - loss: 0.0739
    1137. 

  1137. -> test with GAN.predict
    1138. 

  1138. GAN tn, fp: 271, 17
    1139. 

  1139. GAN fn, tp: 0, 8
    1140. 

  1140. GAN f1 score: 0.485
    1141. 

  1141. GAN cohens kappa score: 0.463
    1142. 

  1142. 

  1143. -> test with 'LR'
    1144. 

  1144. LR tn, fp: 272, 16
    1145. 

  1145. LR fn, tp: 0, 8
    1146. 

  1146. LR f1 score: 0.500
    1147. 

  1147. LR cohens kappa score: 0.479
    1148. 

  1148. LR average precision score: 0.640
    1149. 

  1149. 

  1150. -> test with 'RF'
    1151. 

  1151. RF tn, fp: 287, 1
    1152. 

  1152. RF fn, tp: 1, 7
    1153. 

  1153. RF f1 score: 0.875
    1154. 

  1154. RF cohens kappa score: 0.872
    1155. 

  1155. 

  1156. -> test with 'GB'
    1157. 

  1157. GB tn, fp: 286, 2
    1158. 

  1158. GB fn, tp: 1, 7
    1159. 

  1159. GB f1 score: 0.824
    1160. 

  1160. GB cohens kappa score: 0.818
    1161. 

  1161. 

  1162. -> test with 'KNN'
    1163. 

  1163. KNN tn, fp: 273, 15
    1164. 

  1164. KNN fn, tp: 0, 8
    1165. 

  1165. KNN f1 score: 0.516
    1166. 

  1166. KNN cohens kappa score: 0.496
    1167. 

  1167. 

  1168. 

  1169. ====== Step 5/5 =======
    1170. 

  1170. -> Shuffling data
    1171. 

  1171. -> Spliting data to slices
    1172. 

  1172. 

  1173. ------ Step 5/5: Slice 1/5 -------
    1174. 

  1174. -> Reset the GAN
    1175. 

  1175. -> Train generator for synthetic samples
    1176. 

  1176. -> create 1117 synthetic samples
    1177. 

  1177. -> retrain GAN for predict
    1178. 

  1178. Epoch 1/10
    1179. 

  1179. 
  1/116 [..............................] - ETA: 25s - loss: 0.0743
 38/116 [========>.....................] - ETA: 0s - loss: 0.0884 
 73/116 [=================>............] - ETA: 0s - loss: 0.0850
110/116 [===========================>..] - ETA: 0s - loss: 0.0791
116/116 [==============================] - 0s 1ms/step - loss: 0.0778
    1180. 

  1180. Epoch 2/10
    1181. 

  1181. 
  1/116 [..............................] - ETA: 0s - loss: 0.0363
 38/116 [========>.....................] - ETA: 0s - loss: 0.0710
 69/116 [================>.............] - ETA: 0s - loss: 0.0761
101/116 [=========================>....] - ETA: 0s - loss: 0.0811
116/116 [==============================] - 0s 1ms/step - loss: 0.0768
    1182. 

  1182. Epoch 3/10
    1183. 

  1183. 
  1/116 [..............................] - ETA: 0s - loss: 0.0358
 38/116 [========>.....................] - ETA: 0s - loss: 0.0754
 74/116 [==================>...........] - ETA: 0s - loss: 0.0704
109/116 [===========================>..] - ETA: 0s - loss: 0.0745
116/116 [==============================] - 0s 1ms/step - loss: 0.0754
    1184. 

  1184. Epoch 4/10
    1185. 

  1185. 
  1/116 [..............................] - ETA: 0s - loss: 0.0418
 35/116 [========>.....................] - ETA: 0s - loss: 0.0607
 66/116 [================>.............] - ETA: 0s - loss: 0.0581
101/116 [=========================>....] - ETA: 0s - loss: 0.0697
116/116 [==============================] - 0s 2ms/step - loss: 0.0750
    1186. 

  1186. Epoch 5/10
    1187. 

  1187. 
  1/116 [..............................] - ETA: 0s - loss: 0.0339
 37/116 [========>.....................] - ETA: 0s - loss: 0.0802
 74/116 [==================>...........] - ETA: 0s - loss: 0.0665
112/116 [===========================>..] - ETA: 0s - loss: 0.0746
116/116 [==============================] - 0s 1ms/step - loss: 0.0734
    1188. 

  1188. Epoch 6/10
    1189. 

  1189. 
  1/116 [..............................] - ETA: 0s - loss: 0.0166
 38/116 [========>.....................] - ETA: 0s - loss: 0.0877
 74/116 [==================>...........] - ETA: 0s - loss: 0.0885
110/116 [===========================>..] - ETA: 0s - loss: 0.0775
116/116 [==============================] - 0s 1ms/step - loss: 0.0759
    1190. 

  1190. Epoch 7/10
    1191. 

  1191. 
  1/116 [..............................] - ETA: 0s - loss: 0.0169
 37/116 [========>.....................] - ETA: 0s - loss: 0.0686
 73/116 [=================>............] - ETA: 0s - loss: 0.0739
109/116 [===========================>..] - ETA: 0s - loss: 0.0715
116/116 [==============================] - 0s 1ms/step - loss: 0.0724
    1192. 

  1192. Epoch 8/10
    1193. 

  1193. 
  1/116 [..............................] - ETA: 0s - loss: 0.0503
 38/116 [========>.....................] - ETA: 0s - loss: 0.0653
 73/116 [=================>............] - ETA: 0s - loss: 0.0600
109/116 [===========================>..] - ETA: 0s - loss: 0.0714
116/116 [==============================] - 0s 1ms/step - loss: 0.0727
    1194. 

  1194. Epoch 9/10
    1195. 

  1195. 
  1/116 [..............................] - ETA: 0s - loss: 0.0322
 35/116 [========>.....................] - ETA: 0s - loss: 0.0769
 68/116 [================>.............] - ETA: 0s - loss: 0.0814
103/116 [=========================>....] - ETA: 0s - loss: 0.0745
116/116 [==============================] - 0s 1ms/step - loss: 0.0710
    1196. 

  1196. Epoch 10/10
    1197. 

  1197. 
  1/116 [..............................] - ETA: 0s - loss: 0.2495
 24/116 [=====>........................] - ETA: 0s - loss: 0.0789
 61/116 [==============>...............] - ETA: 0s - loss: 0.0667
 99/116 [========================>.....] - ETA: 0s - loss: 0.0719
116/116 [==============================] - 0s 2ms/step - loss: 0.0698
    1198. 

  1198. -> test with GAN.predict
    1199. 

  1199. GAN tn, fp: 272, 16
    1200. 

  1200. GAN fn, tp: 0, 9
    1201. 

  1201. GAN f1 score: 0.529
    1202. 

  1202. GAN cohens kappa score: 0.507
    1203. 

  1203. 

  1204. -> test with 'LR'
    1205. 

  1205. LR tn, fp: 271, 17
    1206. 

  1206. LR fn, tp: 0, 9
    1207. 

  1207. LR f1 score: 0.514
    1208. 

  1208. LR cohens kappa score: 0.491
    1209. 

  1209. LR average precision score: 0.716
    1210. 

  1210. 

  1211. -> test with 'RF'
    1212. 

  1212. RF tn, fp: 287, 1
    1213. 

  1213. RF fn, tp: 1, 8
    1214. 

  1214. RF f1 score: 0.889
    1215. 

  1215. RF cohens kappa score: 0.885
    1216. 

  1216. 

  1217. -> test with 'GB'
    1218. 

  1218. GB tn, fp: 284, 4
    1219. 

  1219. GB fn, tp: 0, 9
    1220. 

  1220. GB f1 score: 0.818
    1221. 

  1221. GB cohens kappa score: 0.811
    1222. 

  1222. 

  1223. -> test with 'KNN'
    1224. 

  1224. KNN tn, fp: 273, 15
    1225. 

  1225. KNN fn, tp: 0, 9
    1226. 

  1226. KNN f1 score: 0.545
    1227. 

  1227. KNN cohens kappa score: 0.524
    1228. 

  1228. 

  1229. 

  1230. ------ Step 5/5: Slice 2/5 -------
    1231. 

  1231. -> Reset the GAN
    1232. 

  1232. -> Train generator for synthetic samples
    1233. 

  1233. -> create 1117 synthetic samples
    1234. 

  1234. -> retrain GAN for predict
    1235. 

  1235. Epoch 1/10
    1236. 

  1236. 
  1/116 [..............................] - ETA: 18s - loss: 0.0136
 42/116 [=========>....................] - ETA: 0s - loss: 0.1155 
 81/116 [===================>..........] - ETA: 0s - loss: 0.0971
116/116 [==============================] - 0s 1ms/step - loss: 0.0884
    1237. 

  1237. Epoch 2/10
    1238. 

  1238. 
  1/116 [..............................] - ETA: 0s - loss: 0.1570
 37/116 [========>.....................] - ETA: 0s - loss: 0.0740
 74/116 [==================>...........] - ETA: 0s - loss: 0.0831
112/116 [===========================>..] - ETA: 0s - loss: 0.0844
116/116 [==============================] - 0s 1ms/step - loss: 0.0864
    1239. 

  1239. Epoch 3/10
    1240. 

  1240. 
  1/116 [..............................] - ETA: 0s - loss: 0.0356
 39/116 [=========>....................] - ETA: 0s - loss: 0.0876
 69/116 [================>.............] - ETA: 0s - loss: 0.0871
104/116 [=========================>....] - ETA: 0s - loss: 0.0870
116/116 [==============================] - 0s 1ms/step - loss: 0.0864
    1241. 

  1241. Epoch 4/10
    1242. 

  1242. 
  1/116 [..............................] - ETA: 0s - loss: 0.0154
 38/116 [========>.....................] - ETA: 0s - loss: 0.0974
 77/116 [==================>...........] - ETA: 0s - loss: 0.0836
116/116 [==============================] - 0s 1ms/step - loss: 0.0844
    1243. 

  1243. Epoch 5/10
    1244. 

  1244. 
  1/116 [..............................] - ETA: 0s - loss: 0.0466
 38/116 [========>.....................] - ETA: 0s - loss: 0.0786
 77/116 [==================>...........] - ETA: 0s - loss: 0.0751
115/116 [============================>.] - ETA: 0s - loss: 0.0827
116/116 [==============================] - 0s 1ms/step - loss: 0.0827
    1245. 

  1245. Epoch 6/10
    1246. 

  1246. 
  1/116 [..............................] - ETA: 0s - loss: 0.0196
 39/116 [=========>....................] - ETA: 0s - loss: 0.0865
 76/116 [==================>...........] - ETA: 0s - loss: 0.0843
115/116 [============================>.] - ETA: 0s - loss: 0.0828
116/116 [==============================] - 0s 1ms/step - loss: 0.0827
    1247. 

  1247. Epoch 7/10
    1248. 

  1248. 
  1/116 [..............................] - ETA: 0s - loss: 0.0141
 40/116 [=========>....................] - ETA: 0s - loss: 0.0868
 80/116 [===================>..........] - ETA: 0s - loss: 0.0815
116/116 [==============================] - 0s 1ms/step - loss: 0.0825
    1249. 

  1249. Epoch 8/10
    1250. 

  1250. 
  1/116 [..............................] - ETA: 0s - loss: 0.0163
 41/116 [=========>....................] - ETA: 0s - loss: 0.0896
 81/116 [===================>..........] - ETA: 0s - loss: 0.0752
116/116 [==============================] - 0s 1ms/step - loss: 0.0797
    1251. 

  1251. Epoch 9/10
    1252. 

  1252. 
  1/116 [..............................] - ETA: 0s - loss: 0.0168
 39/116 [=========>....................] - ETA: 0s - loss: 0.0937
 78/116 [===================>..........] - ETA: 0s - loss: 0.0822
116/116 [==============================] - 0s 1ms/step - loss: 0.0817
    1253. 

  1253. Epoch 10/10
    1254. 

  1254. 
  1/116 [..............................] - ETA: 0s - loss: 0.2440
 40/116 [=========>....................] - ETA: 0s - loss: 0.0965
 78/116 [===================>..........] - ETA: 0s - loss: 0.0795
116/116 [==============================] - 0s 1ms/step - loss: 0.0781
    1255. 

  1255. -> test with GAN.predict
    1256. 

  1256. GAN tn, fp: 281, 7
    1257. 

  1257. GAN fn, tp: 1, 8
    1258. 

  1258. GAN f1 score: 0.667
    1259. 

  1259. GAN cohens kappa score: 0.654
    1260. 

  1260. 

  1261. -> test with 'LR'
    1262. 

  1262. LR tn, fp: 279, 9
    1263. 

  1263. LR fn, tp: 1, 8
    1264. 

  1264. LR f1 score: 0.615
    1265. 

  1265. LR cohens kappa score: 0.600
    1266. 

  1266. LR average precision score: 0.790
    1267. 

  1267. 

  1268. -> test with 'RF'
    1269. 

  1269. RF tn, fp: 288, 0
    1270. 

  1270. RF fn, tp: 3, 6
    1271. 

  1271. RF f1 score: 0.800
    1272. 

  1272. RF cohens kappa score: 0.795
    1273. 

  1273. 

  1274. -> test with 'GB'
    1275. 

  1275. GB tn, fp: 288, 0
    1276. 

  1276. GB fn, tp: 3, 6
    1277. 

  1277. GB f1 score: 0.800
    1278. 

  1278. GB cohens kappa score: 0.795
    1279. 

  1279. 

  1280. -> test with 'KNN'
    1281. 

  1281. KNN tn, fp: 284, 4
    1282. 

  1282. KNN fn, tp: 0, 9
    1283. 

  1283. KNN f1 score: 0.818
    1284. 

  1284. KNN cohens kappa score: 0.811
    1285. 

  1285. 

  1286. 

  1287. ------ Step 5/5: Slice 3/5 -------
    1288. 

  1288. -> Reset the GAN
    1289. 

  1289. -> Train generator for synthetic samples
    1290. 

  1290. -> create 1117 synthetic samples
    1291. 

  1291. -> retrain GAN for predict
    1292. 

  1292. Epoch 1/10
    1293. 

  1293. 
  1/116 [..............................] - ETA: 17s - loss: 0.1223
 41/116 [=========>....................] - ETA: 0s - loss: 0.0839 
 81/116 [===================>..........] - ETA: 0s - loss: 0.0823
113/116 [============================>.] - ETA: 0s - loss: 0.0802
116/116 [==============================] - 0s 1ms/step - loss: 0.0794
    1294. 

  1294. Epoch 2/10
    1295. 

  1295. 
  1/116 [..............................] - ETA: 0s - loss: 0.0130
 40/116 [=========>....................] - ETA: 0s - loss: 0.0782
 78/116 [===================>..........] - ETA: 0s - loss: 0.0795
116/116 [==============================] - 0s 1ms/step - loss: 0.0782
    1296. 

  1296. Epoch 3/10
    1297. 

  1297. 
  1/116 [..............................] - ETA: 0s - loss: 0.0324
 41/116 [=========>....................] - ETA: 0s - loss: 0.0804
 81/116 [===================>..........] - ETA: 0s - loss: 0.0737
116/116 [==============================] - 0s 1ms/step - loss: 0.0784
    1298. 

  1298. Epoch 4/10
    1299. 

  1299. 
  1/116 [..............................] - ETA: 0s - loss: 0.0629
 43/116 [==========>...................] - ETA: 0s - loss: 0.0695
 84/116 [====================>.........] - ETA: 0s - loss: 0.0667
116/116 [==============================] - 0s 1ms/step - loss: 0.0764
    1300. 

  1300. Epoch 5/10
    1301. 

  1301. 
  1/116 [..............................] - ETA: 0s - loss: 0.1508
 42/116 [=========>....................] - ETA: 0s - loss: 0.0808
 83/116 [====================>.........] - ETA: 0s - loss: 0.0793
116/116 [==============================] - 0s 1ms/step - loss: 0.0758
    1302. 

  1302. Epoch 6/10
    1303. 

  1303. 
  1/116 [..............................] - ETA: 0s - loss: 0.0237
 41/116 [=========>....................] - ETA: 0s - loss: 0.0780
 82/116 [====================>.........] - ETA: 0s - loss: 0.0736
116/116 [==============================] - 0s 1ms/step - loss: 0.0743
    1304. 

  1304. Epoch 7/10
    1305. 

  1305. 
  1/116 [..............................] - ETA: 0s - loss: 0.1141
 41/116 [=========>....................] - ETA: 0s - loss: 0.0780
 79/116 [===================>..........] - ETA: 0s - loss: 0.0833
116/116 [==============================] - ETA: 0s - loss: 0.0739
116/116 [==============================] - 0s 1ms/step - loss: 0.0739
    1306. 

  1306. Epoch 8/10
    1307. 

  1307. 
  1/116 [..............................] - ETA: 0s - loss: 0.0146
 41/116 [=========>....................] - ETA: 0s - loss: 0.0893
 81/116 [===================>..........] - ETA: 0s - loss: 0.0814
116/116 [==============================] - 0s 1ms/step - loss: 0.0728
    1308. 

  1308. Epoch 9/10
    1309. 

  1309. 
  1/116 [..............................] - ETA: 0s - loss: 0.0559
 42/116 [=========>....................] - ETA: 0s - loss: 0.0733
 82/116 [====================>.........] - ETA: 0s - loss: 0.0659
116/116 [==============================] - 0s 1ms/step - loss: 0.0713
    1310. 

  1310. Epoch 10/10
    1311. 

  1311. 
  1/116 [..............................] - ETA: 0s - loss: 0.2322
 41/116 [=========>....................] - ETA: 0s - loss: 0.0673
 82/116 [====================>.........] - ETA: 0s - loss: 0.0739
116/116 [==============================] - 0s 1ms/step - loss: 0.0705
    1312. 

  1312. -> test with GAN.predict
    1313. 

  1313. GAN tn, fp: 276, 12
    1314. 

  1314. GAN fn, tp: 0, 9
    1315. 

  1315. GAN f1 score: 0.600
    1316. 

  1316. GAN cohens kappa score: 0.582
    1317. 

  1317. 

  1318. -> test with 'LR'
    1319. 

  1319. LR tn, fp: 277, 11
    1320. 

  1320. LR fn, tp: 0, 9
    1321. 

  1321. LR f1 score: 0.621
    1322. 

  1322. LR cohens kappa score: 0.604
    1323. 

  1323. LR average precision score: 0.747
    1324. 

  1324. 

  1325. -> test with 'RF'
    1326. 

  1326. RF tn, fp: 287, 1
    1327. 

  1327. RF fn, tp: 1, 8
    1328. 

  1328. RF f1 score: 0.889
    1329. 

  1329. RF cohens kappa score: 0.885
    1330. 

  1330. 

  1331. -> test with 'GB'
    1332. 

  1332. GB tn, fp: 287, 1
    1333. 

  1333. GB fn, tp: 2, 7
    1334. 

  1334. GB f1 score: 0.824
    1335. 

  1335. GB cohens kappa score: 0.818
    1336. 

  1336. 

  1337. -> test with 'KNN'
    1338. 

  1338. KNN tn, fp: 282, 6
    1339. 

  1339. KNN fn, tp: 0, 9
    1340. 

  1340. KNN f1 score: 0.750
    1341. 

  1341. KNN cohens kappa score: 0.740
    1342. 

  1342. 

  1343. 

  1344. ------ Step 5/5: Slice 4/5 -------
    1345. 

  1345. -> Reset the GAN
    1346. 

  1346. -> Train generator for synthetic samples
    1347. 

  1347. -> create 1117 synthetic samples
    1348. 

  1348. -> retrain GAN for predict
    1349. 

  1349. Epoch 1/10
    1350. 

  1350. 
  1/116 [..............................] - ETA: 23s - loss: 0.0367
 41/116 [=========>....................] - ETA: 0s - loss: 0.0893 
 82/116 [====================>.........] - ETA: 0s - loss: 0.0882
116/116 [==============================] - 0s 1ms/step - loss: 0.0800
    1351. 

  1351. Epoch 2/10
    1352. 

  1352. 
  1/116 [..............................] - ETA: 0s - loss: 0.0962
 38/116 [========>.....................] - ETA: 0s - loss: 0.0564
 78/116 [===================>..........] - ETA: 0s - loss: 0.0810
116/116 [==============================] - 0s 1ms/step - loss: 0.0791
    1353. 

  1353. Epoch 3/10
    1354. 

  1354. 
  1/116 [..............................] - ETA: 0s - loss: 0.0237
 40/116 [=========>....................] - ETA: 0s - loss: 0.0854
 78/116 [===================>..........] - ETA: 0s - loss: 0.0770
114/116 [============================>.] - ETA: 0s - loss: 0.0768
116/116 [==============================] - 0s 1ms/step - loss: 0.0781
    1355. 

  1355. Epoch 4/10
    1356. 

  1356. 
  1/116 [..............................] - ETA: 0s - loss: 0.0150
 41/116 [=========>....................] - ETA: 0s - loss: 0.0995
 81/116 [===================>..........] - ETA: 0s - loss: 0.0836
116/116 [==============================] - 0s 1ms/step - loss: 0.0779
    1357. 

  1357. Epoch 5/10
    1358. 

  1358. 
  1/116 [..............................] - ETA: 0s - loss: 0.1093
 42/116 [=========>....................] - ETA: 0s - loss: 0.0780
 85/116 [====================>.........] - ETA: 0s - loss: 0.0791
116/116 [==============================] - 0s 1ms/step - loss: 0.0775
    1359. 

  1359. Epoch 6/10
    1360. 

  1360. 
  1/116 [..............................] - ETA: 0s - loss: 0.0148
 42/116 [=========>....................] - ETA: 0s - loss: 0.0650
 82/116 [====================>.........] - ETA: 0s - loss: 0.0772
116/116 [==============================] - 0s 1ms/step - loss: 0.0763
    1361. 

  1361. Epoch 7/10
    1362. 

  1362. 
  1/116 [..............................] - ETA: 0s - loss: 0.0151
 38/116 [========>.....................] - ETA: 0s - loss: 0.0591
 75/116 [==================>...........] - ETA: 0s - loss: 0.0695
113/116 [============================>.] - ETA: 0s - loss: 0.0765
116/116 [==============================] - 0s 1ms/step - loss: 0.0755
    1363. 

  1363. Epoch 8/10
    1364. 

  1364. 
  1/116 [..............................] - ETA: 0s - loss: 0.0307
 38/116 [========>.....................] - ETA: 0s - loss: 0.0707
 78/116 [===================>..........] - ETA: 0s - loss: 0.0713
112/116 [===========================>..] - ETA: 0s - loss: 0.0737
116/116 [==============================] - 0s 1ms/step - loss: 0.0740
    1365. 

  1365. Epoch 9/10
    1366. 

  1366. 
  1/116 [..............................] - ETA: 0s - loss: 0.1172
 32/116 [=======>......................] - ETA: 0s - loss: 0.0815
 68/116 [================>.............] - ETA: 0s - loss: 0.0794
105/116 [==========================>...] - ETA: 0s - loss: 0.0749
116/116 [==============================] - 0s 1ms/step - loss: 0.0743
    1367. 

  1367. Epoch 10/10
    1368. 

  1368. 
  1/116 [..............................] - ETA: 0s - loss: 0.0110
 36/116 [========>.....................] - ETA: 0s - loss: 0.0780
 71/116 [=================>............] - ETA: 0s - loss: 0.0749
106/116 [==========================>...] - ETA: 0s - loss: 0.0764
116/116 [==============================] - 0s 1ms/step - loss: 0.0747
    1369. 

  1369. -> test with GAN.predict
    1370. 

  1370. GAN tn, fp: 282, 6
    1371. 

  1371. GAN fn, tp: 2, 7
    1372. 

  1372. GAN f1 score: 0.636
    1373. 

  1373. GAN cohens kappa score: 0.623
    1374. 

  1374. 

  1375. -> test with 'LR'
    1376. 

  1376. LR tn, fp: 279, 9
    1377. 

  1377. LR fn, tp: 0, 9
    1378. 

  1378. LR f1 score: 0.667
    1379. 

  1379. LR cohens kappa score: 0.653
    1380. 

  1380. LR average precision score: 0.589
    1381. 

  1381. 

  1382. -> test with 'RF'
    1383. 

  1383. RF tn, fp: 287, 1
    1384. 

  1384. RF fn, tp: 4, 5
    1385. 

  1385. RF f1 score: 0.667
    1386. 

  1386. RF cohens kappa score: 0.658
    1387. 

  1387. 

  1388. -> test with 'GB'
    1389. 

  1389. GB tn, fp: 287, 1
    1390. 

  1390. GB fn, tp: 4, 5
    1391. 

  1391. GB f1 score: 0.667
    1392. 

  1392. GB cohens kappa score: 0.658
    1393. 

  1393. 

  1394. -> test with 'KNN'
    1395. 

  1395. KNN tn, fp: 283, 5
    1396. 

  1396. KNN fn, tp: 0, 9
    1397. 

  1397. KNN f1 score: 0.783
    1398. 

  1398. KNN cohens kappa score: 0.774
    1399. 

  1399. 

  1400. 

  1401. ------ Step 5/5: Slice 5/5 -------
    1402. 

  1402. -> Reset the GAN
    1403. 

  1403. -> Train generator for synthetic samples
    1404. 

  1404. -> create 1116 synthetic samples
    1405. 

  1405. -> retrain GAN for predict
    1406. 

  1406. Epoch 1/10
    1407. 

  1407. 
  1/116 [..............................] - ETA: 20s - loss: 0.0062
 41/116 [=========>....................] - ETA: 0s - loss: 0.0662 
 81/116 [===================>..........] - ETA: 0s - loss: 0.0717
116/116 [==============================] - 0s 1ms/step - loss: 0.0779
    1408. 

  1408. Epoch 2/10
    1409. 

  1409. 
  1/116 [..............................] - ETA: 0s - loss: 0.0280
 41/116 [=========>....................] - ETA: 0s - loss: 0.0696
 81/116 [===================>..........] - ETA: 0s - loss: 0.0824
116/116 [==============================] - 0s 1ms/step - loss: 0.0764
    1410. 

  1410. Epoch 3/10
    1411. 

  1411. 
  1/116 [..............................] - ETA: 0s - loss: 0.0225
 39/116 [=========>....................] - ETA: 0s - loss: 0.0755
 80/116 [===================>..........] - ETA: 0s - loss: 0.0771
116/116 [==============================] - 0s 1ms/step - loss: 0.0749
    1412. 

  1412. Epoch 4/10
    1413. 

  1413. 
  1/116 [..............................] - ETA: 0s - loss: 0.0155
 40/116 [=========>....................] - ETA: 0s - loss: 0.0711
 79/116 [===================>..........] - ETA: 0s - loss: 0.0762
116/116 [==============================] - 0s 1ms/step - loss: 0.0751
    1414. 

  1414. Epoch 5/10
    1415. 

  1415. 
  1/116 [..............................] - ETA: 0s - loss: 0.1543
 41/116 [=========>....................] - ETA: 0s - loss: 0.0606
 81/116 [===================>..........] - ETA: 0s - loss: 0.0660
116/116 [==============================] - 0s 1ms/step - loss: 0.0722
    1416. 

  1416. Epoch 6/10
    1417. 

  1417. 
  1/116 [..............................] - ETA: 0s - loss: 0.2359
 40/116 [=========>....................] - ETA: 0s - loss: 0.0913
 80/116 [===================>..........] - ETA: 0s - loss: 0.0791
116/116 [==============================] - 0s 1ms/step - loss: 0.0719
    1418. 

  1418. Epoch 7/10
    1419. 

  1419. 
  1/116 [..............................] - ETA: 0s - loss: 0.0408
 40/116 [=========>....................] - ETA: 0s - loss: 0.0833
 79/116 [===================>..........] - ETA: 0s - loss: 0.0779
116/116 [==============================] - 0s 1ms/step - loss: 0.0703
    1420. 

  1420. Epoch 8/10
    1421. 

  1421. 
  1/116 [..............................] - ETA: 0s - loss: 0.0979
 40/116 [=========>....................] - ETA: 0s - loss: 0.0738
 81/116 [===================>..........] - ETA: 0s - loss: 0.0795
116/116 [==============================] - 0s 1ms/step - loss: 0.0700
    1422. 

  1422. Epoch 9/10
    1423. 

  1423. 
  1/116 [..............................] - ETA: 0s - loss: 0.0829
 41/116 [=========>....................] - ETA: 0s - loss: 0.0723
 79/116 [===================>..........] - ETA: 0s - loss: 0.0726
116/116 [==============================] - 0s 1ms/step - loss: 0.0694
    1424. 

  1424. Epoch 10/10
    1425. 

  1425. 
  1/116 [..............................] - ETA: 0s - loss: 0.0150
 39/116 [=========>....................] - ETA: 0s - loss: 0.0684
 78/116 [===================>..........] - ETA: 0s - loss: 0.0748
116/116 [==============================] - 0s 1ms/step - loss: 0.0692
    1426. 

  1426. -> test with GAN.predict
    1427. 

  1427. GAN tn, fp: 269, 19
    1428. 

  1428. GAN fn, tp: 1, 7
    1429. 

  1429. GAN f1 score: 0.412
    1430. 

  1430. GAN cohens kappa score: 0.386
    1431. 

  1431. 

  1432. -> test with 'LR'
    1433. 

  1433. LR tn, fp: 275, 13
    1434. 

  1434. LR fn, tp: 0, 8
    1435. 

  1435. LR f1 score: 0.552
    1436. 

  1436. LR cohens kappa score: 0.533
    1437. 

  1437. LR average precision score: 0.354
    1438. 

  1438. 

  1439. -> test with 'RF'
    1440. 

  1440. RF tn, fp: 285, 3
    1441. 

  1441. RF fn, tp: 3, 5
    1442. 

  1442. RF f1 score: 0.625
    1443. 

  1443. RF cohens kappa score: 0.615
    1444. 

  1444. 

  1445. -> test with 'GB'
    1446. 

  1446. GB tn, fp: 282, 6
    1447. 

  1447. GB fn, tp: 3, 5
    1448. 

  1448. GB f1 score: 0.526
    1449. 

  1449. GB cohens kappa score: 0.511
    1450. 

  1450. 

  1451. -> test with 'KNN'
    1452. 

  1452. KNN tn, fp: 275, 13
    1453. 

  1453. KNN fn, tp: 2, 6
    1454. 

  1454. KNN f1 score: 0.444
    1455. 

  1455. KNN cohens kappa score: 0.422
    1456. 

  1456. 

  1457. ### Exercise is done.
    1458. 

  1458. 

  1459. -----[ LR ]-----
    1460. 

  1460. maximum:
    1461. 

  1461. LR tn, fp: 281, 17
    1462. 

  1462. LR fn, tp: 2, 9
    1463. 

  1463. LR f1 score: 0.720
    1464. 

  1464. LR cohens kappa score: 0.709
    1465. 

  1465. LR average precision score: 0.878
    1466. 

  1466. 

  1467. 

  1468. average:
    1469. 

  1469. LR tn, fp: 275.72, 12.28
    1470. 

  1470. LR fn, tp: 0.28, 8.52
    1471. 

  1471. LR f1 score: 0.582
    1472. 

  1472. LR cohens kappa score: 0.564
    1473. 

  1473. LR average precision score: 0.663
    1474. 

  1474. 

  1475. 

  1476. minimum:
    1477. 

  1477. LR tn, fp: 271, 7
    1478. 

  1478. LR fn, tp: 0, 7
    1479. 

  1479. LR f1 score: 0.471
    1480. 

  1480. LR cohens kappa score: 0.446
    1481. 

  1481. LR average precision score: 0.304
    1482. 

  1482. 

  1483. 

  1484. -----[ RF ]-----
    1485. 

  1485. maximum:
    1486. 

  1486. RF tn, fp: 288, 5
    1487. 

  1487. RF fn, tp: 6, 8
    1488. 

  1488. RF f1 score: 0.889
    1489. 

  1489. RF cohens kappa score: 0.885
    1490. 

  1490. 

  1491. 

  1492. average:
    1493. 

  1493. RF tn, fp: 286.48, 1.52
    1494. 

  1494. RF fn, tp: 3.24, 5.56
    1495. 

  1495. RF f1 score: 0.691
    1496. 

  1496. RF cohens kappa score: 0.684
    1497. 

  1497. 

  1498. 

  1499. minimum:
    1500. 

  1500. RF tn, fp: 283, 0
    1501. 

  1501. RF fn, tp: 1, 2
    1502. 

  1502. RF f1 score: 0.353
    1503. 

  1503. RF cohens kappa score: 0.334
    1504. 

  1504. 

  1505. 

  1506. -----[ GB ]-----
    1507. 

  1507. maximum:
    1508. 

  1508. GB tn, fp: 288, 6
    1509. 

  1509. GB fn, tp: 6, 9
    1510. 

  1510. GB f1 score: 0.842
    1511. 

  1511. GB cohens kappa score: 0.837
    1512. 

  1512. 

  1513. 

  1514. average:
    1515. 

  1515. GB tn, fp: 285.76, 2.24
    1516. 

  1516. GB fn, tp: 2.64, 6.16
    1517. 

  1517. GB f1 score: 0.713
    1518. 

  1518. GB cohens kappa score: 0.705
    1519. 

  1519. 

  1520. 

  1521. minimum:
    1522. 

  1522. GB tn, fp: 282, 0
    1523. 

  1523. GB fn, tp: 0, 3
    1524. 

  1524. GB f1 score: 0.353
    1525. 

  1525. GB cohens kappa score: 0.334
    1526. 

  1526. 

  1527. 

  1528. -----[ KNN ]-----
    1529. 

  1529. maximum:
    1530. 

  1530. KNN tn, fp: 285, 15
    1531. 

  1531. KNN fn, tp: 2, 9
    1532. 

  1532. KNN f1 score: 0.857
    1533. 

  1533. KNN cohens kappa score: 0.852
    1534. 

  1534. 

  1535. 

  1536. average:
    1537. 

  1537. KNN tn, fp: 278.64, 9.36
    1538. 

  1538. KNN fn, tp: 0.28, 8.52
    1539. 

  1539. KNN f1 score: 0.650
    1540. 

  1540. KNN cohens kappa score: 0.636
    1541. 

  1541. 

  1542. 

  1543. minimum:
    1544. 

  1544. KNN tn, fp: 273, 3
    1545. 

  1545. KNN fn, tp: 0, 6
    1546. 

  1546. KNN f1 score: 0.444
    1547. 

  1547. KNN cohens kappa score: 0.422
    1548. 

  1548. 

  1549. 

  1550. -----[ GAN ]-----
    1551. 

  1551. maximum:
    1552. 

  1552. GAN tn, fp: 283, 19
    1553. 

  1553. GAN fn, tp: 3, 9
    1554. 

  1554. GAN f1 score: 0.727
    1555. 

  1555. GAN cohens kappa score: 0.717
    1556. 

  1556. 

  1557. 

  1558. average:
    1559. 

  1559. GAN tn, fp: 276.52, 11.48
    1560. 

  1560. GAN fn, tp: 0.72, 8.08
    1561. 

  1561. GAN f1 score: 0.578
    1562. 

  1562. GAN cohens kappa score: 0.560
    1563. 

  1563. 

  1564. 

  1565. minimum:
    1566. 

  1566. GAN tn, fp: 269, 5
    1567. 

  1567. GAN fn, tp: 0, 6
    1568. 

  1568. GAN f1 score: 0.412
    1569. 

  1569. GAN cohens kappa score: 0.386
    1570. 

  1570.